1. business and industrial
2. energy
3. electricity

DOC TOR A L T H E S I S
ISSN 1402-1544
ISBN 978-91-7583-270-8 (print)
ISBN 978-91-7583-271-5 (pdf)
Luleå University of Technology 2015
Yasser Ahmed Mahmood
Department of Civil, Environmental and Natural Resources Engineering
Division of Operation, Maintenance and Acoustics
Availability Analysis of Frequency Converters in
Electrified Railway Systems
Availability Analysis of Frequency Converters in Electrified Railway Systems
Yasser Ahmed Mahmood
Doctoral Thesis
Availability Analysis of Frequency
Converters in Electrified Railway
Systems
Yasser Ahmed Mahmood
Division of Operation and Maintenance Engineering
Luleå University of Technology
April 2015
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Doctoral Disputation
This thesis is being submitted for the degree of Doctor of Philosophy (PhD) in Operation and Maintenance
Engineering. Permission has been obtained from the Faculty Board at Luleå University of Technology to defend the
thesis publicly in room F1031, on Tuesday 28th April 2015 at 10:00 am.
Opponent / Examiner
Professor Hoang Pham, Industrial and Systems Engineering, Rutgers, the State University of New Jersey, USA.
Examination Committee
Professor Stefan Östlund, Electrical Energy Conversion, KTH, Stockholm, Sweden.
Professor Math Bollen, Electric Power Engineering, LTU, Skellefteå, Sweden.
Professor Keshav Dahal, School of Engineering and Computing, University of the West of Scotland, Paisley, UK.
Principle supervisor
Professor Uday Kumar, Operation and Maintenance Engineering, LTU, Luleå, Sweden.
Co-supervisors
Professor Ajit K. Verma, Stord/Haugesund University College, Haugesund, Norway.
Associate Professor Ramin Karim, Operation and Maintenance, LTU, Luleå, Sweden.
Associate Professor Alireza Ahmadi, Operation and Maintenance, LTU, Luleå, Sweden.
Cover photo, Photographer: Michael Erhardsson, Mostphotos
Printed by Luleå University of Technology, Graphic Production 2015
ISSN 1402-1544
ISBN 978-91-7583-270-8 (print)
ISBN 978-91-7583-271-5 (pdf)
Luleå 2015
www.ltu.se
ii
To my Father,
Mother, brothers
and sisters.
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1.
PREFACE
The research work presented in this thesis has been carried out at the Division of Operation and
Maintenance and Luleå Railway Research Center (JVTC) at Luleå University of Technology
(LTU) during the period 2011 to 2015.
First of all, I praise and thank ALLAH (God) for providing me with the persistence, health and
opportunity to complete the research work for this thesis.
I would like here to express my gratitude to the University of Mosul for making it possible for me
to complete my doctoral studies at Luleå University of Technology through their financial
support, and to Trafikverket and LTU for sponsoring and financing this research project.
My gratitude is extended to Professor Uday Kumar, Head of Operation and Maintenance at LTU
and my main supervisor, for providing me with the opportunity to pursue my research at the
Division of Operation and Maintenance. My co-supervisors have been Associate Professor
Alireza Ahmadi, Professor A.K. Verma and Associate Professor Ramin Karim. I would like to
express my deepest gratitude to Associate Professor Alireza Ahmadi for his fruitful guidance and
discussion. I also extend my gratitude to Professor A.K. Verma and Associate Professor Ramin
Karim, for their enrichment of my knowledge through discussions. Moreover, I would like to
express my thanks to Dr Lars Abrahamsson, Dr Johan Odelius, Khalid Atta, and Dr Amir
Garmabaki for their discussions in addition to Professor Nadhir Al-Ansari and Anas Samer for
their help.
I would also like to thank Niklas Fransson, Anders Bülund and the power supply team at
Trafikverket for initiating me into the research problems and for providing relevant data and
information, engaging in technical discussions and sharing their experiences with me. I would
like to extend my thanks to all my colleagues at our division for their support and for the nice
memories that I share with them, and to those who have accompanied me on my PhD journey,
especially Hussan Hamudi, Mustafa Aljumaili, Omar Mohammed, and Yamur Aldouri. I will not
forget my Iraqi friends at LTU who have accompanied me on my PhD journey.
Finally, words cannot fully express my gratefulness to my family, Israa Samer, Ammar and
Abood, for their patience, love and encouragement.
Yasser Ahmed
Luleå, Sweden
April 2015
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2.
ABSTRACT
The increased interest in electrified railways can be explained by their huge capacity, high
efficiency, and low pollution. Today’s electrified railway is expecting higher demands for electric
power for increased speeds, increased traffic volume capacity, and heavier freight loads. This will
in turn impose greater demands on railway infrastructure managers to increase the overall
capacity of their railways without traffic disruption. The increasing demand for railway
transportation services is having a significant effect on important stakeholder requirements, such
as safety, punctuality, dependability, sustainability and costs. This in turn is affecting railway
practices in the areas of operation, maintenance, and modernisation.
The traction power supply system (TPSS) is one of the most important parts of the electrified
railway system due to its responsibility for providing a continuous and adequate electrical power
for electric railway vehicles and its great impact on the availability performance. The TPSS
provides the railway with power either directly with low-frequency generators or indirectly via
frequency converters (FCs). In the converter-fed railway network, frequency converter stations
represent the generation system which converts the electric power from the three-phase 50 Hz
public grid to electric power for the single-phase 16.7 Hz traction grid. Obviously, any
operational problems in the FCs will cause an unavailability of traction power capacity and
consequently entail operational problems for trains, resulting in speed reduction, train delays, or
cancellations. Shortages of traction power may occur due to a loss of capacity or a shortage of
reserve capacity that leads to voltage drop. The installed capacity should be capable of meeting
the system load in the case of unexpectedly large loads, in the event of one or more FC units not
being in service due to forced outages or scheduled maintenance, or when a combination of these
scenarios takes place.
The purpose of the research for this thesis has been to study the availability of frequency
converters in order to prevent and mitigate outage events through optimal decisions for
improvement. To fulfil the stated purpose, empirical data have been collected from reporting
databases, interviews, and documents. Examples of the data gathered are train delay statistics,
failure statistics, and electrical measurements. The data have been analysed on two different
levels, the FC type level and the FC station level, as well as by applying theories related to RAM
analysis. The study has addressed issues regarding availability performance as follows:
improvement of data reporting system planning, reliability modelling, evaluation of availability
performance, and, finally, identification of effective ways to reduce shortages of traction power.
The analysis outcome has revealed the areas where resources must be invested for improvement
to reduce and prevent loss of load due to capacity outages.
Keywords: Availability analysis; RAM; Decision making; Electrified railway; Frequency
converters; Loss of load; Availability improvements; Outage reporting data; Failure data.
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3.
LIST OF APPENDED PAPERS
Paper I
Mahmood, Y.A., Amir.H. S.Garmabaki, Ahmadi, A., Verma, A.K. (2015) “Unit-state
Reliability Model for Frequency Converters in Electrified Railway” (accepted for
publication in IET Generation, Transmission and Distribution).
Paper II
Mahmood, Y.A., Ahmadi, A., Verma, A.K., Karim, R., Kumar, U. (2013)
“Availability and Reliability Performance Analysis of Traction Frequency Converters
– a Case Study”, International Review of Electrical Engineering (I.R.E.E.), Vol. 8,
No. 4, p 1231-1242.
Paper III Mahmood, Y.A., Ahmadi, A., Verma, A.K. (2014) “Evaluation and Selection for
Availability Improvement of Frequency Converters in Electrified Railway”,
International Journal of Power and Energy Systems, Vol. 34, No. 4.
Paper IV Mahmood, Y.A., Abrahamsson, L., Ahmadi, A., Verma, A.K. (2014) “Reliability
Evaluation of Traction Power Capacity in Converter-fed Railway Systems”
(submitted for publication in IET Generation, Transmission and Distribution).
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4. LIST OF RELATED PUBLICATIONS (NOT APPENDED)
During his PhD studies, the author published other research papers which are not appended to this
thesis.
Mahmood, Y.A., Ahmadi, A., Verma, A.K. (2013) “Identification of Frequency Converter
Models for Availability Improvement”, International Journal of Electrical Engineering, Vol. 20,
No. 4, 159-170.
Mahmood, Y.A., Ahmadi, A., Verma, A.K., Srividya, A., Kumar, U. (2013) “Fuzzy Fault Tree
Analysis: a Review of Concept and Application”, International Journal of Systems Assurance
Engineering and Management, Vol. 4, No. 1, 19-32.
Mahmood, Y.A., Ahmadi, A., Karim, R., Kumar, U., Verma, A.K., Fransson, N. (2012)
“Comparison of Frequency Converter Outages: a Case Study on Swedish TPS System”,
Proceedings of World Academy of Science, Engineering and Technology Conference, November
27-29, 2012, Paris, France.
Mahmood, Y.A., Karim, R., Aljumaili, M. (2012) “Assessment of Railway Frequency Converter
Performance and Data Quality using the IEEE 762 Standard”, presented at 2nd International
Workshop and Congress on eMaintenance, December 12-14, 2012, Luleå, Sweden.
Mahmood, Y.A., Ahmadi, A., Verma, A.K. (2013) “Identifying the Critical of Frequency
Converter Models”, presented at International Conference on Condition Monitoring and
Machinery Failure Prevention Technologies, Krakow, June 18-20, 2013, Krakow, Poland.
Mahmood, Y.A.; Stenström, C.; Thaduri, A. (2015) “FTA model for the failure modes and root
causes of the snubber capacitor in GTO Two-level Frequency Converter” (submitted for
publication)
Aljumaili, M., Mahmood, Y.A., Karim, R. (2014) “Assessment of Railway Frequency Converter
Performance and Data Quality using the IEEE 762 Standard”, International Journal of Systems
Assurance Engineering and Management, Vol. 5, No. 1, 11-20.
Amir.H S.Garmabaki, Ahmadi, A., Mahmood, Y.A. (2014) “Reliability Modeling of Multiple
Repairable Units: a Case Study on Frequency Converters” (submitted for publication).
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5.
ABBREVIATIONS
AF
Availability factor
AHP
Analytical hierarchy process
CF
Capacity factor
COPT
Capacity outage probability table
FC
Frequency converter
FOF
Forced outage factor
FOH
Forced outage hour
FOR
Forced outage rate
FORd
Demanded forced outage rate
LOLE
Loss of load expectation
MCDM
Multi criteria decision making
MCF
Mean cumulative function
MCO
Mean cumulative outages
MTBF
Mean time between failure
MVA
Mega Volt Ampere
PTF
Power traffic factor
RAM
Reliability, availability and maintainability
SF
Service factor
SP
State probability
TPSS
Traction power supply system
TTF
Time to failure
TTL
Traction transmission line
TTR
Time to repair
WFOF
Weighted forced outage factor
WFOR
Weighted forced outage rate
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6.
TABLE OF CONTENTS
1.
Chapter 1: Introduction ............................................................................................................. 1
1.1. Background ................................................................................................................. 1
1.2. Statement of Problems ................................................................................................ 4
1.3. Purpose and Objectives ............................................................................................... 5
1.4. Research Questions ..................................................................................................... 5
1.5. Limitations of the Study ............................................................................................. 6
1.6. Author’s Contribution in the Appended Papers .......................................................... 6
1.7. Outline of the Thesis ................................................................................................... 7
2. Chapter 2: Traction Power Supply System ............................................................................... 9
2.1. Introduction to the Traction Power Supply System .................................................... 9
2.2. Centralized and Decentralized Traction Networks ................................................... 10
2.3. Frequency Converter Stations ................................................................................... 12
2.4. Types of Frequency Converter ................................................................................. 12
2.5. Rotary Frequency Converter ..................................................................................... 12
2.6. Static Frequency Converter ...................................................................................... 13
3. Chapter 3: Theoretical Framework ......................................................................................... 17
3.1. Important Definitions ................................................................................................ 17
3.2. Unit-state Reliability Models .................................................................................... 19
3.3. The Performance Measures ...................................................................................... 20
3.4. Capacity Outage Probability Table (COPT) ............................................................. 20
3.5. Reliability Evaluation of Power Capacity ................................................................ 21
3.6. Mean Cumulative Function (MCF) .......................................................................... 22
3.7. The Analytical Hierarchy Process (AHP) ................................................................. 23
4. Chapter 4: Research Methodology.......................................................................................... 25
4.1. Research Methodology ............................................................................................. 25
4.2. Research Design ....................................................................................................... 26
4.3. Data Collection ......................................................................................................... 26
4.4. Data Analysis ............................................................................................................ 28
4.4.1. FC Type level ......................................................................................................... 28
4.4.2. FC Station Level .................................................................................................... 30
4.5. Reliability and Validity ............................................................................................. 30
5. Chapter 5: Summary of the Appended Papers ........................................................................ 33
5.1. Paper I ....................................................................................................................... 33
5.2. Paper II ...................................................................................................................... 34
5.3. Paper III .................................................................................................................... 34
5.4. Paper IV .................................................................................................................... 35
6. Chapter 6: Results and Discussion .......................................................................................... 37
6.1. Results and discussion related to RQ 1 ..................................................................... 37
6.2. Results and discussion related to RQ 2 ..................................................................... 39
6.3. Results and discussion related to RQ 3 ..................................................................... 42
6.4. Results and discussion related to RQ 4 ..................................................................... 48
7. Chapter 7: Research Conclusions ........................................................................................... 55
8. Chapter 8: Research Contributions and Further Work ........................................................... 57
8.1. Research Contributions ............................................................................................. 57
8.2. Further Work............................................................................................................. 58
9. References ............................................................................................................................... 59
10. Appended Papers .................................................................................................................... 65
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1.
CHAPTER 1
INTRODUCTION
This chapter provides an introduction to the topic of traction power supply systems by describing the
background to the present research, defines the research problems, and presents the purpose of the
thesis, the research questions, and the research limitations.
1.1.
Background
The railway is an essential and effective means of mass transportation for passengers and freight,
and facilitates numerous industrial and commercial activities. Railway electrification is the most
energy-efficient way to power trains (Steimel, 2008). The electrified railway system has played
an important role in modern transportation and social development because of its huge capacity,
high efficiency, and low pollution (Chen et al., 2007). Today’s electrified railway sector is
expecting higher demands for enhanced quality of service, as well as competition with other
means of transportation. However, the electrified railway sector is already striving to increase its
capacity to meet the growing demand for the transport of goods and passengers with a high level
of punctuality in its services. This demand will become even higher in the future, which will in
turn impose greater demands on railway infrastructure managers to increase the availability
performance of their railways (Patra, 2007).
In electrified railway systems, the traction power supply system (TPSS) refers to the entire
electrical power supply system feeding the trains of the railway, including the electric grid
managed by the railway administrator (Abrahamsson et al., 2012). The TPSS is an essential
system for the provision of a continuous and adequate electrical traction power for electric
railway vehicles. For practical reasons some TPSS supply single-phase and low-frequency AC
power, and the three-phase power from the public grid is converted into single-phase power
which is fed to the railway. This function is accomplished directly using low-frequency
generators or indirectly via frequency converters (FCs) (Steimel, 2008). The FC is an important
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part of the TPSS due to its responsibility for power production. However, outages do occur in the
power supply system and they account for the vast majority of faults resulting in an interruption
of supply for the end consumers (Setreus et al., 2012). Obviously, operational problems affecting
the FCs or other parts of the TPSS will cause operational interruptions for trains that result in
speed reduction, traffic delays or train cancellations.
TPSS differ to a certain extent from the classical public power transmission and distribution
systems. TPSS can typically not transfer power over vast distances through the catenary system,
so the power needs to be consumed in the vicinity of the power source. Consequently, the outage
of a power converter will lead to a shortage of available power and increase the probability of
loss of load. A shortage of available traction power capacity close to the train loads may lead to
severe voltage drops and, in the worst-case scenario, a brownout. When the voltage drops too
much, the tractive power and the tractive forces of the trains will be limited. Such limitations will
in turn limit the train speeds and may result in train delays. Converter outages can also lead to
complete overloading of the remaining neighbouring converters. Such overloading will in turn
activate protection systems, and eventually lead to situations where trains are facing the problem
of an overhead contact wire which is an open circuit (Abrahamsson, 2008).
Unavailability of traction power capacity leading to traffic disruption is one of the main
challenges for infrastructure managers due to the significant economic consequences for train
operators and society. For example, an analysis of the traffic delay in the Swedish railway
network has shown that the number of delay hours in 2010 due to shortages of traction power
capacity exceeded 65 h (UHte 15-019, 2015). This kind of traffic disruption leads to significant
economic consequences, and the estimated total cost for overall traffic delay reached 4,400
million SEK in 2010, according to estimations made in Natanaelson et al. (2013). The roughly
estimated cost of delays affecting passengers travelling on commuter trains in the Greater
Stockholm area is 1.3 billion SEK each year, including the cost of the extra time which
passengers add to the duration of their journeys in order to allow for possible delays (Nyström,
2008).
To ensure that the power supply is continuously available on demand and that the traction power
reaches the trains without interruption, the performance evaluation of FCs must be taken into
consideration during planning, operation and maintenance. The reliability evaluation of FCs as an
important part of the railway TPSS is becoming a critical issue according to the international
railway reliability standards (EN50126, 1999; IEC 62278, 2002). These standards concern the
requirements for the reliability, availability and maintainability (RAM) evaluation of railways.
The FC in a converter-fed TPSS is like the power generator in a public power supply system.
IEEE STD 762 (2007) standard for generation systems considers productivity in addition to
reliability and availability in performance evaluation of generating units. Therefore, reliability,
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availability, maintainability and productivity evaluation fulfils an important function in analyses
of the availability of electrified railways.
Research on the evaluation of public power system reliability started in the 1930s, has generated
great achievements, and has played an important role in ensuring the safe and stable operation of
today’s power systems. Billinton et al. (2003a; 1984) have discussed in detail the three levels of
power system reliability evaluation: generation capacity evaluation, transmission system
evaluation and distribution system evaluation. The methods of reliability evaluation utilised for
public power supply systems cannot be applied directly to the TPSS of railways because of the
specifics of traction loads (Yang et al., 2009). The main difference between the TPSS and the
public power system is that the loads of the TPSS are moving, so that they vary greatly with
regard to time, location, size, and power factor (Abrahamsson, 2012). Many researchers have
studied the reliability evaluation of the TPSS (Chen et al., 2007; Yang Yuan et al., 2009; Kim et
al., 2008; Min et al., 2009; Duque et al., 2009). These studies have been conducted with a view to
integrating the reliability of individual components into the overall system reliability, through
quantitative evaluation and identification of the critical components or sensitivity analysis. Most
of these studies relate to the transformer-fed TPSS that uses the same frequency as that used by
the public power system. However, still there is a lack of research on frequency converters and
traction power capacity on the converter-fed TPSS.
In order to achieve a high level of operational availability and prevent FC outages, resources must
be allocated properly to the appropriate units to enact the necessary improvements. Establishing
the priority of the need for reliability and maintainability improvement to achieve availability
enhancement is a key aspect of overall system performance. Through doing so, one identifies the
weakest areas of a system and detects where resources must be invested. The multi-criteria
decision making (MCDM) approach was proposed previously and has gained impetus in the field
of power systems in providing an overall assessment of system performance (Xu et al., 2011;
Ferreira et al., 2010; Chatzimouratidis et al., 2009; Wang et al., 2008; Ghofrani-Jahromi et al.,
2010). MCDM can, in addition, be used for improving availability through identification of the
most critical components of electrical power systems and prioritisation in maintenance
scheduling and strategy (Dehghanian et al., 2012; Tanaka et al., 2010; Chen et al. 2008).
A high availability performance from the FCs alone is not enough to prevent unavailability of
traction power capacity. The reserve power capacity must be sufficient to allow for the overhaul
of generating equipment, outages that are not planned or scheduled, and load growth
requirements in excess of the estimates (Billinton et al., 1984). Reliability evaluation of power
capacity concerns an examination of the adequacy of the generating system to meet the load
requirements. Moreover, the most important analytical approach is the system wellbeing
approach, utilised to determine if the system is in a healthy state, marginal state, or risk state
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(Billinton et al., 2003b). This approach is based on a comparison between the generating capacity
and the load level, and takes the forced outage rate for generating units into consideration. FC
outages may lead to an unavailability of traction power in the catenary when there is not enough
reserve power capacity within the FC station. Reliability evaluation of the traction power capacity
is necessary in order to identify the stations which have a shortage of reserve power capacity and
those which are subject to capacity outages.
1.2.
Statement of Problems
The performance evaluation of converter types and stations is crucial for their improvement, in
order to reduce and prevent loss of load. However, there are some challenges to be overcome if
one is to succeed in completing this analysis. One of these challenges is the nonexistence of
outage date, which is required for the reliability analysis of the FC as a generating unit (IEEE
STD 762, 2007). The main problem in this connection is how to extract the missing information
from the failure data and other available information to meet the requirements for outage data.
Moreover, this kind of data is important for selecting or designing an appropriate unit-state
reliability model for the FC in addition to the performance evaluation. Another challenge
concerns the unit-state reliability model, which depends on the time for each state which the unit
resides in, for example the in-service state, outage state, and reserve-shutdown state. The IEEE
four-state reliability model for the gas turbine is a base model for any intermittently operating
unit, but there are some differences between the operational conditions and the characteristics of
the available data for the gas turbine and those for the FC. Therefore, one has to determine the
extent to which the IEEE four-state reliability model is appropriate for the FC. This is an
important challenge, because obtaining satisfactory results is dependent on the use of a suitable
model.
The performance measures used for the different FC types can describe their reliability,
availability, maintainability, and productivity performance. After calculating these performance
measures, the criticality and priority of the need for resource allocation are assessed based on the
relative importance of the evaluating criteria. This is carried out to identify the weaknesses of the
system and determine where resources must be invested for reliability and maintainability
improvement. The challenge in this connection is how to unify the performance measures in
order to find the ranking or the priority of the need for improvement, especially since there are
other factors than the performance measures which should be taken into consideration, such as
similarity.
The performance evaluation of an FC station is based on the capacity outage of the station, which
is equivalent to the outages of an FC unit or FC units that lead to a reduction in the overall
available power capacity of the station. Hence, capacity outage refers to loss of capacity, which
may or may not refer to loss of load or traffic delay. However, a loss of capacity will lead to a
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loss of load when there is a shortage of reserve capacity in the station. Therefore, measurement of
the shortage of reserve power capacity has the same importance as measurement of the capacity
outage for identifying the reason for loss of load. The challenge in this connection is to perform a
measurement of the reserve power capacity with the non-existence of an electrical load profile for
FC stations.
1.3.
Purpose and Objectives
The purpose of this research is to study the availability of frequency converters in an electrified
railway system, and to identify ways to improve the reliability of the power capacity.
The specific objectives of the study can be stated as follows:
x to improve the operation and maintenance data reporting system for frequency converter,
x to develop methodology and tools for availability evaluation and improvement,
x to develop a framework for investigation of loss-of-load events.
1.4.
Research Questions
The following questions have had to be answered to fulfil the objectives of the study.
RQ1. How can outage data be extracted for the performance evaluation of FCs?
RQ2. How appropriate reliability models can be developed for the FC unit and the traction power
capacity?
RQ3. How can the availability performance of FCs be evaluated?
RQ4. What approach can be used to reduce the loss of load?
The research questions are answered by four appended papers, and Table 1.1 shows the
relationship between the research questions and the appended papers.
Table 1.1: Relationship between the appended papers and the research questions.
Papers
I
RQ 1
RQ 2
RQ 3
II
III
IV
RQ 4
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1.5.
Limitations of the Study
x
The results obtained from this study are limited by the information that could be extracted
from the available databases. Some of these databases are based on manually entered data
from the maintenance process which vary in quality to a great extent.
x
The term “frequency converter” is used generally in the thesis as a term including all the
equipment from the input system to the output system, for example the step-up transformer
and the filters. Any event preventing the ability of the converter unit to produce electricity is
covered in the scope of this study. Sometimes the FC cannot provide the power required for
the trains because of problems unrelated to the FC station. Examples of such external events
are failures of the catenary, transformers and boosters, and catastrophic storms, etc.
x
When calculating the traffic delay and traffic density in the reliability evaluation of the
power capacity, there are some factors which the study does not pay attention to, such as the
type of catenary (whether it is BT or AT), the acceleration and deceleration, the train types,
train speeds, driving style and train weight. The study assumed that all of these factors had
the same effect on all the track sections when calculating the traffic delay and traffic density.
1.6.
Author’s Contribution in the Appended Papers
The distribution of the research work for the appended papers was as follows.
Paper I: Yasser Ahmed developed the initial idea of the modified unit-state model in discussion
with Professor A.K. Verma and Dr Alireza Ahmadi. The literature review and the data collection
and analysis were performed by Yasser Ahmed. The equations for the modified model were built
by Yasser Ahmed and Dr Amir Garmabaki. The results of the data analysis were discussed with
Dr Alireza Ahmadi. The first version of the manuscript was prepared by Yasser Ahmed and
improved by suggestions and comments from Professor A.K. Verma.
Paper II: Yasser Ahmed developed the initial idea in discussion with Dr Alireza Ahmadi and
Professor A.K. Verma. The data collection was performed by Yasser Ahmed with the help of Dr
Ramin Karim, while the analysis was carried out by Yasser Ahmed. The results of the data
analysis were discussed with Dr Alireza Ahmadi. The first version of the manuscript was
prepared by Yasser Ahmed and improved by Dr Alireza Ahmadi, while the final version was
improved with suggestions and comments from Professor A.K. Verma and Professor Uday
Kumar.
Paper III: Yasser Ahmed developed the initial idea in discussion with Dr Alireza Ahmadi and
Professor A.K. Verma. The data collection and analysis were carried out by Yasser Ahmed. The
manuscript was written by Yasser Ahmed, while the data analysis was discussed with Dr Alireza
Ahmadi and Professor A.K. Verma, both of whom also suggested adjustments.
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Paper IV: Yasser Ahmed developed the initial idea and the reliability model. The data collection
and analysis were carried out by Yasser Ahmed. The results of the data analysis were discussed
with Lars Abrahamsson and Professor A.K. Verma. The first version of the manuscript was
prepared by Yasser Ahmed and improved with suggestions and comments from Dr Lars
Abrahamsson and Dr Alireza Ahmadi.
1.7.
Outline of the Thesis
The content of this thesis is divided into eight chapters as follows.
Chapter 1: Introduction: This chapter provides an introduction to the topic of traction power
supply systems by describing the background, defining the research problems, and presenting the
purpose of the thesis, the research questions and the research limitations.
Chapter 2: Traction Power Supply System: This chapter presents the traction power supply
system of the electrified railway, with a focus on the source of traction power, frequency
converters, frequency converter stations, types of frequency converter, and traction networks.
Chapter 3: Theoretical Framework: This chapter provides the theoretical framework and the
basic concepts used within this study.
Chapter 4: Research Methodology: This chapter presents the methodology selected for
conducting the study and finding answers to the research questions.
Chapter 5: Summary of the Appended Papers: This chapter provides a summary of the four
papers appended to the thesis.
Chapter 6: Results and Discussion: This chapter discusses the main results obtained in the
study in answer to the research questions.
Chapter 7: Research Conclusions: This chapter provides the main conclusions of the study.
Chapter 8: Research Contributions and Further Work: This chapter presents the main
research contributions and industrially relevant contributions, as well as some ideas about future
work.
References: A list of references is provided.
Appended Papers: This part of the thesis contains the four appended papers.
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2.
CHAPTER 2
TRACTION POWER SUPPLY
SYSTEM
This chapter presents the electrical power supply system in electrified railway networks with a focus on
the source of traction power, frequency converters, frequency converter stations, types of frequency
converter, and traction networks.
2.1.
Introduction to the Traction Power Supply System
Traction power means pulling or gripping power, and today the term is often used synonymously
with tractive power when discussing rail traffic, but it can also be used in connection with cars,
lorries and other vehicles (Östlund, 2012). Early traction power supply systems (TPSS) supplied
DC voltage, but for these systems, excessively thick cables and short distances between feeder
stations were necessary because of the high currents required. Moreover, the feeder stations
required constant monitoring and in many systems, only one train or locomotive was allowed per
track section (Steimel, 2008). In addition, common DC commutating electric motors (i.e.
universal motors) can also be fed with AC, because reversing the current in both the stator and
the rotor does not change the direction of the torque. The introduction of the series-wound
commutator motor allowed electrification with high voltage. However, the inductance of the
windings made the early designs of large motors impractical at standard AC distribution
frequencies. The choice of a low frequency was due to commutation problems in the single-phase
series motors used in the childhood of electric traction (Olofsson, 1993). The frequency had to be
decreased to 16Ҁ Hz to reduce induced voltage to safe values. In addition, AC induces eddy
currents, particularly in non-laminated field pole pieces, which causes overheating and a loss of
efficiency. In order to alleviate such problems, five European countries, namely Germany,
9
Austria, Switzerland, Norway and Sweden, have since 1912 been utilising standardized 15 kV
16Ҁ Hz (one-third of the normal mains frequency) single-phase AC (Steimel, 2008).
Supplying this single-phase and low-frequency power can be accomplished either directly with a
single-phase 16Ҁ Hz generator, or indirectly via a frequency converter (FC). Austria, Switzerland
and most of Germany use 16Ҁ Hz generators, while Sweden, Norway and two states in Germany
use FCs (Steimel, 2008; Olofsson, 1993). The electric trains are fed by a 15 kV overhead contact
line at the nominal frequency of 16Ҁ Hz. The main components of the TPSS are the FC, traction
transformer, traction transmission line, boosters, auto-transformer and catenary, as shown in
Figure 2.1.
6.3
Figure 2.1: Traction power supply with single-phase 16.7 Hz AC.
2.2.
Centralized and Decentralized Traction Networks
There are two types of traction network topologies for feeding the catenary: the centralized and
the decentralized traction network (Laury, 2012). In the centralized network, there is a long highvoltage traction transmission line (TTL) which is located parallel to the catenary to transmit the
traction power from the public grid to the catenary, as shown in Figure 2.2. The decentralized
system does not have any extra line and is used in areas where a TTL is not economically
justified or there are societal barriers against an TTL. Figure 2.3 presents the setup of a
decentralized system (Laury, 2012). The main desired advantages of a TTL with a centralized
network are as follows (Olofsson, 1993; Abrahamsson, 2008):
‡ increased system redundancy,
‡ reduced power flows on the catenaries for long distances,
‡ relief of the pressure on the converter stations close to great loads,
10
‡ reduction of the number of converter stations needed.
In Sweden, for example, the Stockholm-Gothenburg line was electrified in the 1920s and rotary
synchronous-synchronous converters were used, directly feeding the contact wire from the public
grid. After that, decentralized feeding from the 50 Hz national grid using converters became the
means of electrification. In late 1985 it was decided to build a 130 kV TTL from Hallsberg to
Jörn (about 900 km), the reason being the heavy steel transports from Luleå to Borlänge
(Olofsson, 1993). This line is working with two phases and at 2×66 kV (132 kV) and 16.7 Hz,
with two-phase traction transformers connected to the catenary system approximately every 50
km, as shown in Figure 2.2, and supplying 15 kV at each point of connection. Both network
topologies are fed via rotary and static converters. These converters are placed individually, or in
groups in large depots, i.e. converter stations (Laury, 2012).
132 kV, 50 Hz
M
M
G
G
3a
1a
a
3a
1a
a
50 km
132 kV, 16.7 Hz
15 kV, 16.7 Hz
Figure 2.2: Centralized network topology in the TPSS (Laury, 2012).
132 KV, 50 HZ
M
M
G
G
3a
a
1a
3a
a
1a
15 kV, 16.7 Hz
Figure 2.3: Decentralized network topology in the TPSS (Laury, 2012).
11
2.3.
Frequency Converter Stations
In the Swedish railway network, the TPSS comprises around 134 FC units covering around 9,543
km of electrified railway (NES TS02, 2009). There are ten different models of the static and
rotary types of FC, and 76 rotary and 58 static converters are in operation. The FC units are
distributed among the 45 FC stations (NES TS02, 2009). Most converter stations are either rotary
or static stations, while some stations have both the static and rotary types of converter units
(Mahmood et al., 2012). The distribution of the stations along the railway lines and the distances
between any two stations depends on the network topology, the load demand, and the capacity of
the stations in question.
2.4.
Types of Frequency Converter
The rotary FC is the converter traditionally used and it has been in operation in Sweden since
1915 (Olofsson, 1993). However, over the past four decades, static FCs have been put into
service, sometimes in addition to and sometimes as a replacement for rotary converters. Sweden
has a large number of rotary FCs feeding the traction network, operating side-by-side with static
converters to cover the electrified railway network in the Swedish railway system. The rotary
converter still appears to exhibit sufficient performance to justify its continued operation.
2.5.
Rotary Frequency Converter
Rotary converters are equipped with a three-phase synchronous motor and have a single-phase
synchronous generator connected via a common shaft. Therefore, the rotary converter can also be
called the motor-generator (MG) set, as shown in Figure 2.4. The conversion process starts with
three-phase 50 Hz AC input power to the motor. The motor converts electric energy to
mechanical energy via the shaft, which turns a generator. The generator then converts the
mechanical energy back to electrical energy, but in one phase and at 16.7 Hz, and this power is
finally supplied to the load. The type of rotary converter used in Sweden is a synchronoussynchronous motor generator set made by ABB Ltd (NES TS02, 2009).
6.3kV
50Hz
3-phase Synch.
12-pole Motor
1-phase Synch.
4-pole Generator
M
G
15kV
16.7Hz
Figure 2.4: The rotary FC as a motor-generator set.
As Table 2.1 shows, there are three versions of rotary converters, namely the Q24/Q25, Q38/Q39
and Q48/Q49, but for simplicity we call them the Q24, Q38 and Q48, with a rated power reach of
12
3.2, 5.8 and 10 MVA, respectively. The power capacity provided by the whole population of this
type for the system reaches 501 MVA. There are 76 converters of this type in the Swedish TPSS
and they are located in 25 rotary converter stations, each of which contains 2-4 converters. The
advantages offered by this type of converter are line isolation, harmonic cancellation, power
factor correction, and voltage conversion with balanced, smooth and controlled power output.
This converter generates a sine wave in exactly the same way as the utility does. In addition, the
converter exerts a very friendly load on the public grid and creates reactive power to support the
voltage just like a normal synchronous machine. The harmonics and modulations of the voltage,
current, frequency or loads in AC networks can be filtered by the rotary FC, which serves as a
natural filter, thus rendering an extra filter unnecessary (Pfeiffer et al., 1997; Shi, 2011).
Table 2.1: Summary of the basic information for all the types of FCs considered in this study.
2.6.
FC
Type
No. of
converters
Power
capacity
(MVA)
Total power
capacity
(MVA)
Number
of stations
Structure
Rotary
Q24
Q38
Q48
11
45
20
3.2
5.8
10
35,2
261
200
8
23
11
Motor- generator
Static
YOQO
YRLA
TGTO
Mega I
Mega II
Cegelec
Areva
16
3
12
14
4
2
7
13-15
8,2
14
15
6,7
15
15
234
24,6
168
210
27
30
105
7
1
6
7
2
1
4
Cyclo-convertor
PWM-thyristor-2level
PWM-GTO-2level
PWM-GTO-3level
Static Frequency Converter
Cyclo-converters and DC-link voltage source inverters are utilised in parallel with synchronoussynchronous motor-generator sets. The cyclo-converter was the first static converter to come into
operation and it was introduced in the middle of the 1970s (Olofsson, 1993). The DC-link
converter offers some performance advantages over cyclo-converters, but a trade-off against cost
and complexity always precedes its installation (Pfeiffer et al., 1997). The static or so-called
solid-state FC is a power electronic device and has no moving parts. The development of the
solid state FC is mainly constrained by the hardware of the power electronic devices and the
microcontroller chips. Static converters are based on power electronics, and the output voltage is
controlled by appropriate control of the converter. The output voltage does not follow a purely
sinusoidal wave and comes with harmonics. These harmonics need to be filtered. Due to the
unavoidable power-pulsation in the single-phase grid, a resonant circuit tuned to twice the line
frequency is connected in parallel to the DC-link capacitor. Modules for filtering and isolation
must be installed, as a result of which the solid state FC becomes bulkier and heavier (Pfeiffer et
13
al., 1997). The output voltage can be controlled by increasing the delay angle, alpha, at the
thyristor. Alpha is the angle between the current crossing zero and the actual firing of the
thyristor. By controlling alpha, the mean value of the output voltage can be controlled. In
particular, this means that the active power flows from the public grid to the railway system can
be controlled.
In Sweden, the most common type of static converter is the DC-link type (see Table 2.1), and
there are two types, namely the two-level and the three-level converter as shown in Figure 2.5
and Figure 2.6, respectively. Figure 2.7 shows a diagram of a cyclo-converter and there are 16
units of this type in the Swedish TPSS. The total number of static FC units installed in Sweden is
58 and they are distributed among the 18 converter stations (NES TS02, 2009).
3a filter
3a Transformer
Rectifier
DC-link
Inverter
11a Tr.
11a filter
3a
50 Hz
1a
16.7 Hz
Figure 2.5: The main construction of the two-level dc-linked frequency converter.
3a filter
3a Transformer
Rectifier
DC-link
3a
50 Hz
11a TranInverter sformer
11a filter
sf
1a
16.7 Hz
Figure 2.6: The main construction of the three-level dc-linked frequency converter.
14
3a filter
3a Transformer
Cyclo-converter
1a Transformer 11a filter
3a
50 Hz
1a
16.7 Hz
Figure 2.7: The main construction of the cyclo-converter.
15
16
3.
CHAPTER 3
THEORETICAL FRAMEWORK
This chapter provides a brief presentation of the theoretical framework and the basic concepts used in
this study; for further details see the appended papers.
3.1.
Important Definitions
Basic concept definitions are important for description of the reliability of traction power supply
systems, and this section briefly describes and explains the terms used in the present study.
x The reliability of an item is the probability that the item will perform its required function
under given conditions for a stated time interval (Birolini, 2010). In the context of the present
research, the reliability of frequency converters (FCs) is a measure of their ability to convert
the traction power at a specific time (IEEE STD 762, 2007). The reliability of a traction power
supply system can be defined as its ability to provide a continuous supply of electrical power
of adequate quality without causing safety hazards and train delays (Sagareli, 2004).
x The availability of a product is its ability to be in a state to perform a required function under
given conditions at a given instant of time or over a given time interval, assuming that the
required external resources are provided. Availability is measured in terms of the fraction of
time during which a unit is capable of providing service and accounts for the outage frequency
and duration (IEEE STD 762, 2007).
x The maintainability of an item is its ability, under given conditions of use, to be retained in or
restored to a state in which it can perform a required function, when maintenance is performed
under given conditions and using stated procedures and resources (IEC, 2007).
17
x Productivity is measured in terms of the total power produced by a plant with respect to its
potential power production; a plant can comprise a unit or a number of units (IEEE STD, 762
2007).
x Measurements of the loss-of-load probability, or the loss-of-load expectation, as it is also
called, are used in generation planning studies (Contaxis et al., 1989). Such measurements are
the most widely used probabilistic technique for evaluating the adequacy of a given generation
configuration (Billinton et al., 1984). The loss-of-load expectation denotes the expected
number of hours per year during which the system load will exceed the available generating
capacity (Fockens et al., 1992). In the context of the present research, the loss of load is
assumed to be the amount of traffic delay (in hours) for each track section over a specific time
period (study period).
x Loss of capacity indicates a loss of generation (due to forced outage) which may or may not
result in a loss of load, depending upon the generating capacity reserve margin and the system
load level. A loss of load will occur only when the capability of the generating capacity
remaining in service is exceeded by the system load level (Billinton et al., 1984).
x A unit-state is a particular unit condition or status of a generating unit that is important for
collecting data for unit performance. The number of unit states depends on the reliability
model used, which describes the actual state of each unit at all times. Many state descriptions
can be used to define the current status of a power generating unit according to the unit states
and time designations of IEEE STD 762 (2007) as shown in Figure 3.1. The definitions of a
few states are as follows:
x Available: A converter is capable of converting service, regardless of whether it is actually in
service and regardless of the capacity level that can be provided, it’s divided into two states:
-
In-service: The unit is ‘electrically connected to the system’ and is available for providing
output power.
Reserve-shutdown: The unit is available, but not in service because the system-load
demands do not require the unit to operate.
x Unavailable: The converter does not provide power because of a forced outage or planned
maintenance, and it’s divided into two states:
-
Planned-outage: The state in which a converter is not providing output power due to
planned maintenance on a subsystem or component during a time period.
Forced-outage: A converter is unavailable owing to catastrophic component failure
resulting in inadequate or complete lack of output power. An outage that results from
emergency conditions directly associated with the requirement that a component be taken
out of service immediately, either automatically or as soon as switching operations can be
performed, or an outage caused by improper operation of equipment’s or human errors.
18
Period Hours
Available
Hours
Service
Hours
Reserve Shutdown
Hours
Unavailable
Hours
Planned Outage
Hours
Unplanned (Forced)
Outage Hours
Active Repair
Time
Delay
Time
Figure 3.1: Main components of operational period time.
3.2.
Unit-state Reliability Models
The generating unit is usually modelled using a series of states in which the generating unit can
reside. The unit can transit from one state to another in accordance with certain actions. These
states and the possible transitions mimic the operating behaviour of the generating unit. The
resulting model is used to incorporate the generating unit unavailability in the power system
reliability evaluation (Billinton et al., 2004). The basic model for a generating unit is a two-state
representation in which the unit resides either in the up-state (operating state) or in the downstate. The two-state model is the model normally used and it provides a reasonable representation
of a base-load power unit; this is a unit which usually provides a continuous supply of electricity
throughout the year and is only to be turned off during periodic maintenance, upgrading, overhaul
or service. A peaking power unit is operated for only a small number of hours per day or a
fraction of the days in the year and spends a considerable amount of time in a reserve-shutdown
state (Ortega-Vazquez et al., 2008). The two-state model is not suitable for the representation of
intermittently operating units and results in an unreasonably high unavailability index estimate
for a peaking unit (Billinton et al., 2004). The four-state model, which recognizes the
intermittently operating characteristics of a peaking unit and which was introduced by IEEE, is
shown in Figure 3.2. The available state and the unavailable state are each divided into additional,
mutually exclusive states. The available state is divided into the in-service state and the reserveshutdown state, while the unavailable state is divided into the planned-outage state and the
unplanned-outage state. The three-state reliability model which was used in this study is based on
the IEEE four-state model; i.e. the four-state was modified to a three-state model. In Section 6.1
and Paper I it is maintained that the three-state model is a reasonable reliability model for the FC
and the reasons for this modification are discussed.
19
“0”
Reverse
Shutdown
(1-Ps)/T
1/D
μ
(1/r)
μ
(1/r)
“1”
Forced Out
Not Needed
“2”
In Service
1/T
1/D
O
(1/m)
“3”
Forced Out
Needed
Figure 3.2: Four-state model for the peaking power unit.
Notation:
3.3.
D = average in-service time per occasion of demand (h).
T = average reserve-shutdown time between periods of need (h).
r = average repair time per forced outage occurrence (h).
m = average in-service time between occasions of forced outage, excluding forced outages as a result of
failure to start (h).
Ps = probability of a starting failure resulting in an inability to serve the load during all or part of a
demand period. Repeated attempts to start during one demand period should not be interpreted as more
than one failure to start.
Reserve-shutdown state = the state where a unit is available, but not in service.
The Performance Measures
Probabilistic reliability evaluation of power systems is becoming important in the electric utility
environment due to its ability to represent the system behaviour as stochastic in nature (Billinton
et al., 1984). In the field of electrical power system reliability, there are several probabilistic
measurement techniques in use for describing the performance of power units in terms of their
reliability, availability, maintainability and productivity. IEEE STD 762 (2007) introduced
performance measures and provided terminology and indexes for use in reporting the reliability,
availability, and productivity of electric generating units. The standard provides a methodology
for the interpretation of electric generating unit performance data from various systems and
facilitates comparisons among different systems. It also standardizes terminology for reporting
electric generating unit reliability, availability, and productivity performance measures. The
performance measures used within the present study are the service factor (SF), availability factor
(AF), forced outage factor (FOF), forced outage rate (FOR), and capacity factor (CF). Paper II
used these measures for performance evaluation of FC types and Paper IV used them for FC
stations. Some of the obtained results of performance measures are presented and discussed in
Section 6.3 for both FC types and stations.
3.4.
Capacity Outage Probability Table (COPT)
The performance measures can be used to provide the probabilistic performance of converter
units and stations. These measures are valuable for the identification of weak areas needing
20
reinforcement through comparison among different systems, but cannot provide information
about the probability of available and unavailable of power capacity. The probability of available
and unavailable capacity can be provided by a capacity outage probability table (COPT) (Wang et
al., 1994). The COPT is a simple array of capacity levels with associated probabilities of capacity
existence, and is one of the most important methods for evaluating the risk of power outages
using the FOR (forced outage rate) index, which represents the probability of generation outage
(Suresh et al., 2012b). This table represents the capacity outage states of the generating units,
together with the probabilities of these states in ascending order. The probability value in the
COPT is the probability of the given amount of capacity being out of service. The cumulative
probability is the probability of finding a quantity of capacity unavailable which is equal to or
greater than the amount unavailable in the state (Al-Abdulwahab, 2010).
The present study used two values from the COPT in the reliability evaluation of the traction
power capacity. The first value is the state probability (SP) when the whole capacity is available.
The second value is the probability when the whole capacity is out of service, which is called the
risk value. The individual SP is calculated according to equations 3.1 and 3.2, respectively
(Suresh, 2012a).
N
state probability (SPi )
– prob
k
(3.1)
k 1
probk
FORk
.if stateik
0
probk
1 FORk .if stateik
1
(3.2)
where probk is the SP of unit k being functional in the station, FORk is the forced outage rate
(FOR) of unit k, and N is the number of FC units (Suresh, 2012a). The SP and risk value reflect
the capacity outage of the FC station and they are used for reliability evaluation of the traction
power capacity. In Section 6.1 the use of the SP and risk in the reliability model of the traction
power capacity is discussed, while in Section 6.4 and Paper IV the results of using the COPT, SP
and risk values in the reliability evaluation of the power capacity are discussed.
3.5.
Reliability Evaluation of Power Capacity
The performance measures and the probability of available capacity obtained using the COPT
represent the capacity outage. The capacity outage indicates the loss of capacity, which may or
may not result in a loss of load. This is because all of this information is related to the power
generating model, without comparison with information related to the load model and load
profile. In the public power system, in order to analyze the reasons for the unavailability of power
capacity and study the probability of loss of load, the reliability of the power capacity has to be
determined. The COPT is combined with the system load characteristic to give an expected risk
21
of loss of load. This kind of analysis depends on the forced outage rate (FOR) to find the COPT
from the point of view of loss of generation. There are three important levels of power conversion
capacity for the analysis. These three levels are the base-load, peak, and installed power
capacities. These three important power capacity levels are in Figure 3.3 plotted against a typical
load profile. The load profile can be compared to the installed and reserve capacity according to
the COPT results to find the loss-of-load expectation (LOLE). The purpose of this analysis is to
know the reasons for power unavailability on the basis of the two main models: the power
generating model and the load model. The most important use for this information is to analyse
whether the system is in a healthy state, marginal state or risk state (Billinton et al., 2003b). The
state is identified as a healthy state when the available reserve is equal to or more than the largest
unit. A marginal state is identified when the available reserve is less than the required capacity
reserve, but greater than zero, while a risk state is identified when the load exceeds the available
generation. Paper IV discussed how to identify the risk state of the capacity in order to find the
reason for loss of load. The results of the reliability evaluations of the traction power capacity are
presented and discussed in Section 6.1 and Section 6.4.
Power Capacity
Installed Capacity
Reserve
Capacity
Peak Load
Load Profile
Base Load
Time
Figure 3.3: Installed, reserve, peak-load and base-load power capacity levels.
3.6.
Mean Cumulative Function (MCF)
Non-parametric methods can provide a non-parametric graphical estimate of the number of
recurrences of repairs/failures per unit and per the whole population, versus the utilization/age
(Nelson, 1998). The model used to describe a population of systems in this study is based on the
mean cumulative failure (MCF) at the system age t, as well as the mean cumulative outages
(MCO). The MCF plot is a simple, easy, informative and widely used plot of the cumulative
number of recurrent failures versus the time, and the time can be the operating time counted from
the date and time of installation, or the age given in calendar time (in hours and days) or as a
number of cycles or power (Nelson, 1998). A key advantage of the MCF plot is that it provides a
single summary plot for a group of systems, a plot which is more easily compared with other
22
MCF plots for different populations and provides the ability to make statistically meaningful
comparisons between multiple populations (Halim et al., 2008; Al Garni, 2009). This method can
be used to identify quickly trends, anomalous systems, unusual behaviour, maintenance
performance, etc. Furthermore, with the help of the MCF, decision-makers can identify areas in
which extra resources are needed to improve the system performance (Trindade et al., 2005;
Misra, 2008; Foucher, 2002). The total observed cumulative number of failures for the whole
population is given by:
n
N (t )
¦ N (t )
(3.3)
i
i 1
The mean cumulative number of failures ( ), also described as the mean cumulative function
(MCF), can be estimated as:
P t E ª¬ N t º¼
n
¦P t (3.4)
i
i 1
During the late 1980’s, Nelson (Nelson 1988) provided a suitable point-wise estimator for the
MCF, ( ). Having the recurrence times
times; i.e. unit has = 1, … ,
highest,
<
,…,<
recurrences. Order the recurrence times from the lowest to the
j
ª ¦ n G i tk d i tk º
i 1
»
n
1
¬ ¦ i 1G i tk »¼
¦ ««
k
(3.5)
(t ) is as follows:
( )=
where
be the unique recurrence
. The MCF can then be estimated as:
Pˆ t j where
of all the units , let
1, if unit is still functioning,
0, otherwise,
( ) is the number of recurrences for unit at the time
.
The above equations show the MCF versus the time; see Section 6.3 and Paper II, which used the
MCF to calculate the mean cumulative outage (MCO) versus the operating time and the
converted power for different types of frequency converter.
3.7.
The Analytical Hierarchy Process (AHP)
The analytical hierarchy process (AHP) was introduced by Saaty in 1977 and is a structured
technique for deriving priority scales to arrive at a scale of preferences amongst a set of
alternatives (Saaty, 1980). The AHP is implemented by reducing complex decisions to a series of
simple comparisons and rankings and then synthesizing the results through quantifiable and/or
intangible criteria. The AHP employs pairwise comparison in which experts compare the
importance of two factors according to a relatively subjective scale. Therefore, a questionnaire is
necessary for the construction of an importance judgment matrix based on the relative importance
23
given by the experts (Ahmadi et al., 2010; Ta et al., 2000; Saaty 2008). This questionnaire utilises
vague linguistic patterns and can be easily filled in by experts. A much better representation of
these linguistic patterns can be developed as a data set and then refined using the evaluation
methods of fuzzy set theory (Özda÷o÷lu et al., 2007). Another important consideration in the
AHP is the notion of consistency. Consistency is the degree to which the perceived relationship
between elements in the pairwise comparisons is maintained (Ahmadi et al., 2010; Ta et al.,
2000; Saaty, 2008).
The local weights of the attributes and the local weights of the alternatives with respect to the
attributes are combined according to AHP to obtain the total weight and overall rating of the
alternatives as follows:
n
Wi
¦ w w i
a
ij
c
ij
1, 2, }., n (3.6)
i 1
where Wi is the total weight of alternative i, and waij is the local weight of alternative i with
respect to attribute j. Further, wcij is the local weight of criterion j, and n denotes the number of
alternatives.
The disadvantage of the AHP is that it does not take into account the uncertainty associated with
the mapping of human judgment to a number for different criteria. The experts do not have to
express how many times an attribute is more important. They express their opinion through
simple linguistic judgments via a questionnaire which is easy to understand and can be filled in
quickly; consequently, the resulting weights are more accurate (Locatelli et al., 2012). Therefore,
judgements based on fuzzy methodology can consider the uncertainty and vagueness of the
subjective perception (Mikhailov et al., 2004; Mahmood et al., 2013). The opinions of different
experts must be aggregated. A fuzzy AHP can be implemented in two different ways: by
supporting the whole process until the final prioritization or by determining only the importance
weights for the criteria (Locatelli et al., 2012). In Paper III, the fuzzy linguistic variable is used in
order to aggregate the expert opinions by determining the importance weights for the criteria. In
Section 6.4 the result of the AHP is presented for the ranking of the ten types of frequency
converter according to their need for improvement, with a view to reducing the loss of load.
24
4.
CHAPTER 4
RESEARCH METHODOLOGY
This chapter presents theories of research methodology in general and, in particular, the research
methodology selected as suitable for conducting this study and finding answers to the research
questions.
4.1.
Research Methodology
The term “research” can be defined as a systematic and scientific activity undertaken to establish
a fact, a theory, a principle or an application (Kothari, 2011), involving a systematic examination
of observed information, and performed to find answers to problems. Moreover, research can be
described as a stepwise process of finding answers to questions (Neuman, 2002). However, in
order to conduct research, it is essential to choose a suitable research methodology. The research
methodology selected is the link between thinking and evidence (Sumser, 2000) and, accordingly,
it refers to the way in which the problem is approached in order to find an answer to research
questions (Taylor et al., 1984). Research methodology has many dimensions, and research
approaches constitute a part of research methodology; the scope of research methodology is
wider than that of research approaches and methods.
Research approaches can be divided into quantitative and qualitative research approaches, as well
as a combination of both approaches. The quantitative approach emphasizes the measurement and
analysis of causal relationships between different variables, and involves the use of numbers,
counts, and measures of things. The qualitative approach aims at giving an explanation of causal
relationships between different events and consequences, and generally utilises data in the form
of words (or more precisely data involving nominal and ordinal scales) (Sullivan, 2001). Both
quantitative and qualitative research methodologies have been applied in the research presented
in this thesis. A quantitative approach has been used to explore the performance measures of
25
frequency converters (FCs), types of converters and converter stations to identify the worst
stations and types for improvement. A qualitative approach has been used to explore the minor
and major failures in the failure database and to describe the different operational risks of failure.
A mixture of qualitative and quantitative approaches has been used in establishing the hierarchy
of overall performance measures.
Through discussions, interviews and consultations with experts and based on the knowledge
created through an extensive literature study, the research questions were identified.
Consequently, the associated research objectives were defined according to the research
problems. The methodologies proposed in the appended papers were developed in consultation
with experienced practitioners who judged their relevance and validity. Some conclusions could
be drawn with the support of the empirical data and comparisons could be made with the theory.
In short, this thesis concerns applied research whose purpose is to develop and provide
methodology and tools for availability performance, with the aim of achieving traffic services
without disruption due to unavailability of power capacity.
The study of research methodology entailed training in gathering, arranging and indexing
information material, and training in techniques for the collection of data appropriate to particular
problems, in the use of statistics, questionnaires and controlled experimentation, and in recording,
sorting, and interpreting evidence. This was motivated by the necessity of designing the research
methodology for the research problem at hand, as different problems require different
methodologies (Kothari, 2011).
4.2.
Research Design
The research design is the conceptual structure within which the research is conducted, and it
constitutes the blueprint for the collection, measurement and analysis of data. As such the design
includes an outline of what the researcher will do, from writing the hypothesis and its operational
implications to the final analysis of data (Kothari, 2011). A research design is a plan for getting
from the starting point to the finish line, with the starting point being defined as the initial set of
questions to be answered, and the finish line as some set of conclusions about (answers to) these
questions. There are a number of major steps to be taken between the starting point and the finish
line, including the collection and analysis of relevant data (Yin, 2013). The research design for
this thesis is presented in Figure 4.1 as a flowchart showing the main steps of the research plan
which was to be followed to obtain the answers to each research question initiated by the two
available databases.
4.3.
Data Collection
Data can be defined as the empirical evidence or information that scientists carefully collect
according to rules or procedures to support or reject theories (Neuman, 2002). Data can be
26
categorised as quantitative (i.e. expressed as numbers) or qualitative (i.e. expressed as words,
objects or pictures). In this study, the data related to the operation of the converters investigated
were gathered using three different sources, i.e. reporting databases, interviews and documents.
Figure 4.1: Flowchart of the research design for this study.
There are two kinds of database belonging to Trafikverket (the Swedish Transport
Administration) and containing data related to FCs: 0Felia and GELD (UHte 15-019, 2015).
27
0Felia is a failure reporting database that gathers data on all the significant events for the systems,
i.e. dates of events, descriptions of faults, failed components, descriptions of maintenance, repair
times, maintenance release times, corrective maintenance actions, etc. The GELD database
consists of readings of electrical measurements such as converter input/output current, station
input/output current, converter active power, station active/reactive power and station
input/output power (Mahmood et al., 2012; Aljumaili et al., 2014). The GELD database contains
around ten thousand text files for each converter for one year, and each text file contains the
electrical readings for one day (with readings made every 10 seconds) and is named according to
the date of that day. The data cover the 76 rotary and 58 static converters which are distributed
among the 45 FC stations in the Swedish railway system and concern the period from January
2007 until June 2010.
Interviews were performed with experienced practitioners at Trafikverket, including both project
and field technicians in the Operation and Maintenance Departments. The interviews and
discussions supported the researchers in solving the challenges faced concerning the data
collection, filtering and validity. The documentation used as a source of data consisted of
different descriptions, policies, and procedures pertaining to the operation and maintenance of
converters in addition to the statistical information about train traffic services (UHte 15-019,
2015). Expert judgments were used as a qualitative assessment tool to find out the relative
importance of the evaluating criteria for the overall converter performance, and in this connection
the analytical hierarchy process (AHP) was implemented.
4.4.
Data Analysis
In order to fulfil the objective of the study and answer the research questions, it was decided that
the study should be performed on two levels, namely the converter type level and the station
level, as shown in Figure 4.2. Since the TPSS was considered to be a complex system similar to
the public power system due to the system levels included, an analysis on both the FC type level
and the FC station level was deemed necessary to cover all the factors affecting the availability of
the traction power capacity.
4.4.1. FC Type level
The procedure used for the FC type level included four steps, which are shown in Figure 4.3. The
outage data is very important for studying any electric power unit such as a generator unit and a
power converter. Accordingly, the first step was to process the outage data in order to find a
proper reliability model based on the processed data. Processing the outage data and finding a
proper reliability model are discussed in Paper I. The second step was to calculate the reliability,
availability, maintainability, and productivity performance by using performance measures; see
Paper II for more details.
28
Availability
Analysis
FC Station
Level
Performance
Evaluation
FC Type Level
Reason for Loss
of Load
RAM+P
Evaluation
Availability
Improvement
Figure 4.2: The structure of the data analysis.
The third step was to unify the performance measures and select the worst converter type based
on the priority of the need for availability improvement. To assign weights to the criteria, the
experiences of field experts were used as an effective body of know-how to assist the estimation.
To clearly understand the performance of the FC types, a comparative chart, fuzzy expert
opinions, and multi-criteria decision making were used and discussed in Paper III. Step four
relates to the TGTO type, whose reliability and availability performance was lower than that of
all the other types. The TGTO needed reliability and maintainability improvement and the
reasons for its lower availability performance needed to be identified.
Process the outage data and find a proper
reliability model for the FC.
Calculate the probabilistic performancee
measures for all the FC types.
Select and identify the FC type(s) with lower
l
performance for improvement purposes.
Perform an in-depth study of the type(s)
s)
selected for improvement.
Figure 4.3: The steps of the proposed approach for the FC type level.
29
4.4.2. FC Station Level
A loss of load may occur due to the outage of converter units (loss of capacity) or due to a
shortage of reserve power capacity in the FC station. Therefore, the availability performance of
the FC stations was also evaluated to identify the worst performance for improvement purposes.
The first step for this level was to calculate the performance measures for the stations. The
second step was to compile the capacity outage probability table (COPT) for any station which
had a higher traffic delay. The COPT values, together with the traffic delay and traffic density,
were then used in the third step, which involved creating a proposal for a reliability model for the
power conversion capacity. The main advantage of this reliability model was the identification of
the reasons for the power shortage of each station. The fourth step for this level, as shown in
Figure 4.4, was to discuss the reasons which were found based on the results of the performance
measures and the reliability model and to determine where the resources for improvement should
be invested.
Calculate the performance measures for the
FC stations.
Calculate the capacity outage probability table
(COPT).
Reliability evaluation of traction power
capacity.
Identify the reasons for loss of load.
Figure 4.4: The steps of the proposed approach for the FC station level.
4.5.
Reliability and Validity
Reliability has been achieved in a study if the same results are obtained when the research
methodology used for that study is applied under identical or very similar conditions by another
researcher. High reliability may be seen as the absence of errors and biases in the study (Yin,
2013). The validity of a study concerns whether the study investigates the phenomenon of interest
or not, and denotes how well an idea about reality “fits” the actual reality (Neuman, 2002). In
order to enhance the reliability of the present study, the data collection and classification
methodology has been described in the appended papers. Furthermore, the theoretical concepts
30
used as support in the different parts of the study are explained in each paper. The information
and data on which this study is based have been extracted from papers in peer-reviewed journals
and conference proceedings in the field of electrified railways and Trafikverket’s databases.
These reliable sources, in addition to the application of well-established availability analysis
techniques and consultations with railway experts about the applied methodology and the results
obtained, contribute to increasing the study’s validity. The reliability and validity of the work
performed within this study were also continuously monitored and reviewed, both internally by
the research group at the Division of Operation and Maintenance Engineering at LTU, and
externally through oral and written presentation of the study’s progress to Trafikverket.
31
32
5.
CHAPTER 5
SUMMARY OF THE APPENDED
PAPERS
This chapter provides a summary of the four papers appended to the thesis, and describes their main
contribution towards answering the research questions.
5.1.
Paper I
Mahmood, Y.A., Amir.H. S.Garmabaki, Ahmadi, A., Verma, A.K. (2015) “Unit-state Reliability
Model for Frequency Converters in Electrified Railway” (accepted for publication in IET
Generation, Transmission and Distribution).
Paper I addressed two important issues concerning frequency converters (FCs) in electrified
railway networks, namely outage data and an appropriate reliability model. The main properties
of outage data are the unit state, the time spent in each state, and the power/energy data, which
are used to evaluate the converter performance. However, the failure data from the 0felia
database do not meet the requirements for outage data. Therefore, a method was proposed for
extracting the information missing in the failure database from electrical measurements, in order
to meet the requirements for outage data. The second issue concerns an appropriate unit-state
reliability model for the FC. The IEEE four-state model was modified to a three-state model, this
is due to the lack information related to needed and not-needed states. Moreover, considering the
general FOR is enough for reliability evaluation of power capacity instead of using FORd as is
done for public power systems. Therefore, the study combined the “forced-out, needed” state and
the “forced-out, not needed” state into one state, namely the “forced-outage” state.
33
5.2.
Paper II
Mahmood, Y.A., Ahmadi, A., Verma, A.K., Karim, R., Kumar, U. (2013) “Availability and
Reliability Performance Analysis of Traction Frequency Converters – a Case Study”,
International Review of Electrical Engineering (I.R.E.E.), Vol. 8, No. 4, p 1231-1242.
The purpose of Paper II was to evaluate the reliability, availability and maintainability (RAM) of
the ten models of converters used in the Swedish railway system, and to compare these ten types
according to their RAM performance. The key performance indicators introduced by the IEEE
STD 762 methodology were used to measure and compare the RAM performance of the
converters. Moreover, the mean cumulative function (MCF) was used for monitoring and
comparing the field reliability of the converters, and analysing it versus the operating time,
capacity factor and converted power. This part of the study shows that the use of the MCF for
power units should be based on converted power for only those types which have the same power
capacity, because the MCF indicates the usage intensity more than the operating hours.
Therefore, the capacity factor should be used to standardize the power capacity differences and to
obtain the advantages of both power and time.
5.3.
Paper III
Mahmood, Y.A., Ahmadi, A., Verma, A.K. (2014) “Evaluation and Selection for Availability
Improvement of Frequency Converters in Electrified Railway”, International Journal of Power
and Energy Systems, Vol. 34, No. 4.
The aim of Paper III was to unify the performance measures and to rank the FC types based on
the priority of their need for improvement, so that the availability performance could be
enhanced. Availability analysis and system importance analysis are key aspects of the study of
overall system performance. They identify the weakest areas of a system and indicate where
resources must be invested to achieve the maximum system improvement. Paper III proposed an
approach that involves the calculation of performance measures and then the comparison of these
measures via a comparative chart. Thereafter, the opinions of experts are collected and
aggregated through linguistic variables, and the FC types are then identified using the analytical
hierarchy process. Reliability, availability and maintainability (RAM) were used as criteria to
identify the FC types. In addition, productivity was also considered, as well as the sharing of
converted power, and the capacity factor and service factor measures.
34
5.4.
Paper IV
Mahmood, Y.A., Abrahamsson, L., Ahmadi, A., Verma, A.K. (2014) “Reliability Evaluation of
Traction Power Capacity in Converter-fed Railway Systems” (submitted for publication in IET
Generation, Transmission and Distribution).
The aim of Paper IV was to propose a practical approach to analysis of the reliability of traction
power capacity in order to identify the reasons for loss of load due to unavailability of the power
capacity. There are two main reasons for unavailability of the power conversion capacity, namely
loss of capacity and shortage of reserve capacity, and these two phenomena can occur in
combination. The outages of power system equipment will significantly weaken the TPSS, cause
operational problems and ultimately lead to traffic disruption due to speed reduction, train delays,
or cancellations. In this paper, it is assumed that train delays are caused by converter outages,
shortages of reserve power capacity for unexpected train loads, or a combination of the two. The
availability performance of the converter station, as well as the reliability evaluation of the
conversion power capacity, is considered. A power-traffic factor (PTF) is proposed in order to
aggregate and quantify the train power demands. The PTF can be related to the availability of the
power conversion capacity in order to identify areas with shortages of reserve capacity. Paper IV
found that the power unavailability that leads to train delays in some regions of the system is due
to the shortage of reserve power capacity.
35
36
6.
CHAPTER 6
RESULTS AND DISCUSSION
This chapter discusses the main results of the study and presents the answers to the research questions.
6.1.
Results and discussion related to RQ 1
The first research question of the study was formulated as follows: “How can outage data be
extracted for the performance evaluation of FCs?” This question was answered mainly by the
research presented in Paper I.
In Paper I, a method was proposed which utilised data processing to extract the missing
information from available databases, in order to meet the requirements for outage data. Outage
data are required for evaluation of the reliability, availability, maintainability, and productivity
performance of electrical generating units. The main properties of outage data are the unit state,
the time designation, and the power/energy terms (IEEE STD 762, 2007). A unit state is the
particular unit condition or status which describes the actual state of the unit, for example the inservice state. The time designation represents the time that the unit has spent in each state; for
example, the term “service hours” (SH) represents the number of hours which the unit has spent
in the in-service state. The power/energy terms relate to the power production, which can be
expressed in terms of maximum capacity, derating capacity, and available capacity. The main
existing database for frequency converters (FCs) in the Swedish railway system is the 0felia
database, which contains failure data. The failure data recorded in 0felia concern all the
significant events for the systems and include dates of events, descriptions of faults, the failed
components, descriptions of maintenance, repair times, maintenance release times, corrective
maintenance actions, etc. In addition, there is the GELD database, in which electrical
measurements are recorded and are available as text files containing the following data: the
input/output converter current, input/output station current, station input/output power, etc.
37
The information that can be extracted from the failure data concerns the available and unavailable
states only, while information concerning states within the available and unavailable states is
missing completely. This means that information concerning states such as the in-service,
reserve-shutdown, planned-outage, and forced-outage states cannot be extracted from the 0felia
database. Therefore, as a means of acquiring this missing information, in the present study a
method was proposed which utilised data processing to extract the missing information from both
0felia and GELD. Figure 6.1 shows the steps of the proposed method of data processing, which
depends on a comparison between the 0felia and GELD databases. A Matlab programing code
was built to perform this comparison between the two databases to obtain the time-to-failure
(TTF) and time-to-repair (TTR) values. The code was designed to read tens of thousands of text
files for each converter in order to extract the missing information. Initially the code needs to
select the station name, the unit number and the time/date according to the time/date of failure in
0felia. The code will then calculate the ON and OFF timing, as well as the amount of converted
power within the TTF period. For more details regarding the data extraction process, see Paper I.
The obtained information is related to the in-service and reserve-shutdown states within the
available state, in addition to the planned-outage and forced-outage states within the unavailable
state.
Select station and unit
Read date & time of failure from 0felia
Calculate the time to failure (TTF) and
d
time to repair (TTR)
Select the file from GELD according
ng tto
o
date & time
Calculate the on-time and off-time
me
within TTF period from GELD
By comparsion, find the in-service,
e
reserve-shutdown, and converted power
Figure 6.1: Steps of the data extraction process.
The purpose of this data extraction process is to establish outage data for the performance
evaluation of converters. Figure 6.2 shows the level of information extraction from the existing
failure data in 0felia and the level of information extraction from both databases using the data
38
extraction process. Using the information from the failure data only, without the information
from the data extraction process, would be the equivalent of applying the two-state reliability
model for intermittently operating units. This would lead to completely irrational performance
results, because the two-state model is only reasonable for base-load units, while FCs are
intermittently operating units.
Active Unit
Available Hours
(AH)
In-service Hours
(SH)
Reserve-shutdown
Hours (RSH)
Unavailable Hours
(UH)
Planned-outage
Hours (POH)
Unplanned (Forced)
outage Hours (FOH)
Extracted
from
failure data
Extracted
from both
databases
Figure 6.2: Unit states, time designations and levels of data extraction.
After identifying the unit states and time designations, the reliability, availability, and
maintainability can be evaluated with probabilistic measures and plotted using the MCF versus
time. However, using the MCF plot versus the operating time does not reflect the effect of the
usage intensity for electrical power units. It is preferable to plot the MCF based on the converted
power. Hence, the amount of converted power within the TTF period is an important piece of
data for reliability and productivity measures and needs to be calculated. The data extraction
process using Matlab code was also used to calculate the amount of converted power from GELD
data according to the timing recorded in 0felia for each unit. The results of this data processing
provided all the data required for the performance evaluation of the converter types and stations.
6.2.
Results and discussion related to RQ 2
The second research question of the study was formulated as follows: “How appropriate
reliability models can be developed for the FC unit and the traction power capacity?” This
question was answered by the research presented in Papers I and IV.
FCs used in electrified railway systems normally operate in an intermittent mode. Therefore, in
Paper I, a reliability model was developed for the FC as an intermittently generating unit. There
are two main reasons for using a reliability model for the FC. The first reason is to measure the
performance of FC units, while the second is to incorporate the FC unit unavailability in the
reliability evaluation of the traction power capacity. A generating unit can be modelled by a
series of states in which the unit can reside (Billinton et al., 2004). The four-state reliability
39
model was developed by IEEE for a peaking and intermittently operating gas turbine generating
unit, see Section 3.2. This model divides the forced-outage state into two segments: the “forcedout, needed” state and the “forced-out, not needed” state. The “needed” and “not needed”
additions here refer to the demanded power load. The purpose of separating the “forced-out,
needed” state is to find the demand forced outage rate (FORd), which is used in the reliability
evaluation of the power capacity. Due to the lack of data for separation of the time associated
with the “forced-out, needed” state from that associated with the “forced-out, not-needed” state,
IEEE developed a demand factor for estimation of FORd from the general FOR (IEEE Task
Group, 1972).
In fact, the data regarding the time for the “forced-out, needed” and “forced-out, not needed”
states were not extractable from the field data for FCs available for the present study. Moreover,
the demand factor proposed by IEEE provides unreasonable results for converters due to the
difference between the FC and the gas turbine concerning start-up attempts. In addition, utilising
the general FOR is sufficient for reliability evaluation of the traction power capacity. The use of
FORd is, of course, needed for using regular reliability evaluation of power systems.
Hence, a three-state reliability model was used instead of the four-state model by combining the
“forced-out, needed” state and the “forced-out, not needed” state into a single forced-outage state,
and using the in-service state, representing the production state, and the reserve-shutdown state,
representing the state where the unit is available, but without demand. Figure 6.3 shows the threestate reliability model developed in Paper I for the FC unit. The developed model provides a
better description of the operation of the FC than the two-state and four-state models, without
losing the main properties of the reliability performance in a real operational environment.
“0”
ReserveShutdown
μ
(1/r)
(1-Ps)/T
1/D
μ
(1/r)
Ps/T
“1”
In-Service
O
(1/m)
“2”
Forced-Outage
Figure 6.3: Three-state model for a peaking FC unit.
Notation in Figure 6.3: D=average in-service time (h), T=average reserve-shutdown time (h),
r=average repair time per forced outage occurrence (h), m=average in-service time between
occasions of forced outage (h), Ps=probability of failure on starting the unit.
40
The results for the availability performance of the FCs alone are not sufficient for an examination
of the traction power capacity for meeting the load requirement. The reserve power capacity must
be sufficient to allow for the overhaul of generating equipment, outages that are not planned or
scheduled, and load growth requirements in excess of the estimates (Billinton et al., 1984). This
can be examined by using the reliability model of the power capacity, which can also be used to
identify the reasons for loss of load. Paper IV dealt with the reliability model of the traction
power capacity in electrified railway networks.
In the public power generation system, the reliability model of the power capacity depends on
both the power generating model and the load model, see Section 3.5. The purpose of the
reliability model of the power capacity is to find the probability of loss of load and to examine the
adequacy of the power capacity for meeting the load. This task can usually be accomplished by
using the capacity outage probability table (COPT) based on the values of FORd, and by
comparing the generating capacity with the load profile (Billinton et al., 2003b).
In the case of the TPSS, the load depends completely on the number of trains and the traffic
density. Hence, the term “loss of load” refers to the traffic delay. Therefore, in this study a
reliability model of the traction power capacity was proposed which depends on the power-traffic
factor (PTF), as shown in Figure 6.4. In Paper IV, the PTF measure was developed in order to
obtain a simple and aggregated measurement of the train as a load in the railway power system.
The PTF measure can be used for comparisons between the power conversion capacity and the
loads from traffic expected to affect the converter stations. This measure can be employed to
evaluate whether or not the reserve capacity is sufficient to cover the demanded traffic when
there is a lack of information from the traditional load model. Using the developed PTF measure
for the railway system, it is not necessary to compare the loss of capacity with the load using the
same electrical unit (MVA). Therefore, the COPT can be calculated using the general FOR that
was obtained from the three-state model for the converter unit.
In the part of the study covered in Paper IV, two values from the COPT were selected, namely the
state probability (SP) and the risk. The SP represents the probability of the whole station capacity
being available, while the risk represents the probability of the whole station capacity being
unavailable. There are two main reasons for the unavailability of traction power capacity that
cause train delays, and these two reasons can act in combination. The first main reason is the
outage of one or several units in a converter station. The second main reason is the occurrence of
loads exceeding the available power conversion. Both an outage and a load higher than expected
may lead to a shortage of reserve conversion capacity and failure to meet the demanded power.
41
Train
Traffic
FC Model
Power
Capacity
Loss of
Capacity
Loss of
Load
Shortage of
Power Capacity
SP+Risk
Delay
PTF
PTF, risk & SP
Comparisons
Reliability Evaluation
of Power Capacity
Figure 6.4: The proposed model for reliability evaluation of the power conversion capacity.
Therefore, in Paper IV it is proposed that the reliability of the power capacity depends on the risk
and the SP, which refer to the loss of capacity, and on the PTF, which refers to the shortage of
reserve capacity within the station. The result that can be achieved through reliability evaluation
of the power capacity is an identification of the stations that have a severe loss of capacity and
stations that have a shortage of reserve power capacity.
6.3.
Results and discussion related to RQ 3
The third research question of the study was formulated as follows: “How can the availability
performance of FCs be evaluated” This question was answered on the FC type level mainly by
the research presented in Paper II and III. On the FC station level, RQ 3 was answered by the
research documented in Paper IV.
The mean cumulative function (MCF) was used to calculate the mean cumulative outage (MCO)
and plotted against the converted power and the operating time. The reliability performance of a
generating unit depends on operational factors such as the usage intensity (electrical load), usage
mode, and operating environment (Blischke et al., 2011). In this study, the reliability trends by
MCF for the FC units have been studied, with the usage intensity being considered. The operating
time and the converted power have been considered as the main factors reflecting the usage
intensity. Figure 6.5a and 6.5b show the MCO for the rotary types versus the operating time and
the converted power, respectively. It is evident that the reliability trends (MCO) for the Q24, Q38
and Q48 show different slopes when the MCO is plotted against the operating time and when it is
plotted against the converted power. The Q38 shows a negative trend when the MCO is plotted
against the converted power, while it shows a constant trend (constant outage rate) when the
MCO is plotted against the operating time. The Q24 shows a negative trend (decreasing outage
rate) in both figures, with a higher rate when the MCO is plotted against the converted power.
42
The Q48 also shows a negative trend (decreasing outage rate) in both figures, with a higher rate
when the MCO is plotted against the operating time. A decreasing outage rate reflects a reliability
improvement and an increasing outage rate reflects a reliability decrease for the unit concerned.
14
14
14
14
12
10
12
10
10
9
Q24
Q48
8
8
MCO
M
CO
MCO
M
CO
10
9
Q38
6
Q48
Q38
8
8
66
4
4
2
2
0
Q24
00
5000
5000
10000
10000
0
20000 hh
20000
15000
15000
00
200000000
2
400000000
4
600000000
6
Operating hour
a
8 0
800000000 1000000000 1200000000 1400000000
8
b
10
12
*10 VA
Converted power
Figure 6.5: The MCO vs the operating hours in (a) and vs the converted power in (b) for the
rotary types.
In fact, these differences in behaviour are due to differences in the power capacity. The power
capacity of the Q24 is 3.2 MVA, while that of the Q38 is 5.8 MVA and that of the Q48 is 10
MVA. In order to overcome the effect of the power capacity and the operating time, the capacity
factor is used to normalize the power capacity differences and to obtain the advantages of both
power and time.
The MCO versus the capacity factor for the rotary types is shown in Figure 6.6a, and that
relationship for the static types is shown in Figure 6.6b. The Q24 shows a lower level of
reliability than the other rotary types, with a high positive trend, while the Q38 shows a constant
rate and the Q48 shows a slightly positive trend. The YRLA and TGTO exhibit lower levels of
reliability than the other static types, as shown in Figure 6.6b. The Areva shows a negative trend
and a lower number of outages than the other static and rotary types.
25
25
Q24
10
Q38
20
20
Q48
MCO
CO
MCO
MC
8
YOQC
YRLA
TGTO
Mega I
15Mega
Mega II
M
6Mega
A rev a
C egelec
115
5
6
10
10
4
55
2
0
0.1
0.2
0.3
a
0.4
0.5
0.6
Capacity
C
it ffactor
0
,
00,0
,
0,1
0.1
,
0,2
0.2
b
,
0,3
0.3
,
0,4
0.4
Capacity
C
it factor
f t
Figure 6.6: The MCO vs the capacity factor for the rotary types in (a) and static types in (b).
43
In addition, the following performance measures were used to quantify and compare the
performance of the generating units: the service factor (SF), availability factor (AF), capacity
factor (CF), forced outage factor (FOF), forced outage rate (FOR), and forced outage hours
(FOH). Table 6.1 shows the values of these performance measures for different FC types. This
table indicates that the Mega II has the lowest FOF and FOR compared with all the other types,
while the TGTO has the highest FOF and the Q24 has the highest FOR index. In fact, these
measures assume that the usage intensity is constant over time and that the operational conditions
will stay the same in the future. However, the changes in the reliability of a unit are the prime
factor in the assessment of performance. The problem with using measures per se is that they are
based on the FOH (outage duration), and do not consider the reliability trend. Therefore, in the
present study, the methodology introduced in Figure 6.6 was used to incorporate the reliability
trend in the analysis.
It is evident from Figure 6.6 that the YRLA, TGTO and Q24 have high outage rates (see the
slopes in the figures), which means a higher expected number of outages in future operation in
comparison with the other FC types. In addition, the Mega II shows a reasonably higher AF,
lower FOH and a lower FOF in Table 6.1. However, the MCO curve for the Mega II shows a
positive trend (i.e. an increasing outage rate). Hence, it is expected that the reliability
performance of the Mega II will decrease with usage, which will negatively affect its availability
performance in future operation.
In addition, the best values of the CF and the SF belong to the Q24 and the YRLA, respectively.
The highest and the lowest AF values belong to the Mega II and TGTO, respectively, and these
two are static types.
Table 6.1: Performance measures of the FC types.
Measures
Converter types
YRLA TGTO Mega I
Q24
Q38
Q48
YOQC
Mega II
Areva
Cegelec
AF (%)
98.7
98.7
98.1
99.1
98.7
97.7
98.6
99.6
98.2
98.2
SF (%)
42.4
62.1
51
62.2
80.8
74.9
61.4
70.7
63.2
66.5
CF (%)
60
55
45
43
43
40
38
27
28
40
FOF (%)
1.2
1.2
1.8
0.8
1.2
2.2
1.35
0.39
1.7
1.7
FOR (%)
6.2
2.4
4.2
1.9
1.5
3.1
2.5
0.57
2.9
1.8
FOH (h)
256
238
335
151
223
407
247
71.5
322
325
It should be noted that the MCF does not include the outage duration, while the performance
measures comprise the outage duration in the computation and therefore have the ability to reflect
the capacity outage. While the MCF provides information on the trends, indicating whether the
reliability is improving, deteriorating, or staying normal against the usage intensity, the
performance measures are not able to indicate the trend and future expectations. Therefore, it can
44
be concluded that using both the MCF and the performance measures together will provide a
more complete picture about the current situation and future expectation.
In Paper III, a collective measuring factor was used to aggregate the performance measures as an
overall assessment of the FC types in the form of a ranking (worst-to-best ranking). This
collective performance measure is based on an aggregation of the six performance measures
shown in Table 6.1 in addition to the number of outages. Table 6.2 shows the ranking according
to the FOF, the FOR and the collective performance measure for all the FC types. As can be
clearly seen, the results obtained from the ranking according to the collective performance factor
are somewhat similar to those obtained from the FOR and FOF ranking. The proposed approach
for ranking can be used as a decision support tool by infrastructure managers to analyse the
performance of the FC types, considering the influencing factors, and to identify where resources
must be invested.
Table 6.2: Ranking of the FC types according to the FOR, FOF and performance factor.
No.
FOF
FOR
Collective performance measure
1
2
3
4
5
6
7
8
9
10
TGTO
Q48
Cegelec
Areva
Mega I
Q38
Q24
YRLA
YOQC
Mega II
Q24
Q48
TGTO
Areva
Mega I
Q38
YOQC
Cegelec
YRLA
Mega II
TGTO
Q48
Areva
Q24
YRLA
Cegelec
Mega I
Q38
YOQC
Mega II
It should be noted that, in order to reduce the occurrence of loss of load leading to traffic delay,
the infrastructure manager should identify the reason why the system load is exceeding the
available traction power. The loss of load is also related to the reliability of the power capacity,
which is a built-in property of each station and differs from one station to another.
Therefore, in Paper IV, the MCF was used to plot the mean number of failures at each station; see
Figure 6.7 for the results for the static stations. According to this figure, it is evident that the
MCFs associated with Åstorp and Nässjö have positive trends, meaning that the rate of
occurrence of failures increases with the calendar time. The kind of figure which Figure 6.7
represents can be utilised to support decisions on the performance of maintenance actions,
including decisions as to where the maintenance activities have to be increased to enhance the
overall performance. However, the behaviour of the FCs based on the number of failures does not
refer to the station’s capacity outage, since the number of failures does not give information on
the duration of the outages which caused a loss of load. Therefore, in Paper IV, the AF, the
45
weighted forced outage factor (WFOF), the weighted forced outage rate (WFOR), and the FOH
were used to measure the performance of the 45 FC stations in the Swedish railway system, see
Table 6.3. According to this table, the performance measures of Gällivare, Eldsberga, and
Hässleholm exceeded the threshold of the acceptable level and were considered as the stations
with the lowest performance; see Paper IV for further details.
140
120
MCF
100
80
60
40
20
Fig.
0 5 Cumulative number of outages for the whole population of static stations.
0
5000
10000
15000
20000
25000
Variable
Alingsås
Älvsjö
Ånge
Åstorp
Bastuträsk
Boden
Borlänge
Eldsberga
Esk ilstuna
Hässleholm
Järna
Malmö
Mellansel
Moholm
Nässjö
Ock elbo
Olsk rok en
Tälle
Västerås
Ystad
30000
0 Calendar hours
Figure 6.7: Mean cumulative number of failures for the whole population of static FC stations.
The most important reliability measure usually employed for public power systems is the loss-ofload expectation (LOLE). It can be defined as the expected number of hours per year during
which the system load will exceed the available generating capacity (Fockens et al., 1992). This
definition is relevant to the public power supply system, while in the case of the TPSS, the actual
load is different in that it only consists of trains and is measured in terms of the traffic density in
each track section. In this case the loss of load can be assumed to be the amount of traffic delay in
each section over a specific time period.
According to statistics based on measurements performed in a case study concerning traffic delay
due to the load exceeding the available traction power capacity, the highest traffic delay belongs
to the following stations: Olskroken, Hässleholm, Malmö, Falköping, Moholm, and Mjölby (see
Paper IV). The two main reasons for such loss of load are a shortage of reserve power capacity
and a loss of capacity due to outage of units. Although the Gällivare and Eldsberga stations are
the worst stations according to the four performance measures, there is no loss of load recorded
for these stations. On the other hand, the Hässleholm station has the third worst performance
measures and the second highest loss of load (see Paper IV).
46
Table 6.3: The performance measures of the FC stations.
FC Stations
FOH (h) AF % WFOF %
WFOR % FC Stations FOH (h) AF % WFOF %
WFOR %
Alvesta
88.5
98.4
1.03
1.59
Ösmo
28
99.8
0.2
0.4
Duved
424
97.7
2.3
5.1
Östersund
511.3
97.6
1.3
4.9
Emmaboda
204.7
99.3
0.4
0.82
Alingsås
228.3
98.7
0.9
1
Falköping
94
99.5
0.5
0.7
Bastuträsk
102.5
99.4
0.6
0.8
Gällivare
1191
93.5
6.5
11.3
Boden
280.5
98.5
1.5
2.7
Häggvik
260.2
98.6
1.4
2.2
Borlänge
107.5
99.4
0.6
0.8
0
100
0
0
0
100
0
0
Kil
523.3
97.1
1.26
4.99
Eldsberga
907
95
5
9.6
Kiruna
238.8
98.7
0.58
2.43
Eskilstuna
15.5
99.9
0.1
0.1
Kristinehamn
119.5
99.3
0.7
1
Hässleholm
833
95.4
2
5.1
Mellerud
10.5
99.9
0.1
0.1
Järna
374.3
98
2
2.1
Mjölby
293.5
98.4
1.01
1.9
Mellansel
563.3
96.9
3.1
5.4
Moholm
221.8
98.2
0.8
1.9
Malmö
58
99.7
0.2
0.3
Mora
6.5
100
0.02
0.03
Nässjö
471.3
97.4
2.6
7.7
Murjek
440
97.6
2.4
3.4
Ockelbo
34.3
99.8
0.2
0.3
Olskroken
505.7
97.2
2.8
4.6
Tälle
176.5
99
1
1.5
0
100
0
0
Jakobshyttan
Eksund
0
100
0
0
199.5
98.9
1.1
3.1
Sjömarken
0
100
0
0
Sköldinge
80.3
99.6
0.4
0.5
Ystad
127.5
99.3
0.7
1
Stenbacken
191.3
99
0.38
0.86
Åstorp
223.3
98.8
1.2
1.5
Tornehamn
248.5
98.6
0.8
1.34
Älvsjö
96.8
99.5
0.5
0.9
Uddevalla
65
99.6
0.4
0.4
Ånge
361
98
2
2.3
Varberg
57
99.7
0.13
0.2
Nyköping
Ottebol
Västerås
Moreover, the stations that belong to the category with the highest loss of load, such as
Olskroken, do not belong to the category with the lowest performance measures. It can be
concluded that a low availability performance of a station may or may not refer to a loss of load,
and vice versa. The reason for this is that loss of load depends on the power capacity and the load
capacity. When the base-load level capacity is slightly less than the full traction power capacity,
any outage of one of the FC units will lead to a loss of load; in other words, an FC outage will
result in a loss of load when the reserve capacity is zero. It should be noted that one of the worst
stations according to the performance measures and in terms of loss of load is Hässleholm. This
station has three FC units, one Q48 and two TGTO units. The Q48 and TGTO proved to be the
worst FC types in the overall assessment of the FC types.
47
6.4.
Results and discussion related to RQ 4
The fourth research question of the study was formulated as follows: “What approach can be used
to reduce the loss of load?” This question was answered by the research presented in Papers III
and IV.
The installed generating capacity should be capable of meeting the system load in the event of
capacity outages and the removal of selected generating units for scheduled maintenance. There
are two main reasons for loss of load, the first of which is an outage of one or several units in a
converter station and is called a loss of capacity. The second main reason is an inability of the
available power to meet the load when one or several units are not in service owing to outage or
scheduled maintenance, and this is due to a shortage of reserve capacity. In fact, a loss of load
may also occur due to a combination of both the above-mentioned reasons.
The approach adopted in the study for reducing the loss of load is implemented on two levels, the
FC type level and the station level. On the type level, the loss of load is reduced by addressing the
loss of capacity by increasing the availability performance of the lower performing FC types.
Therefore, an availability performance analysis of the FC types is crucial in order to take
appropriate action to reduce the outages of FC units. In Paper III, an AHP-based multi-criteria
decision making (MCDM) approach was used to rank the performance of the FCs and to
prioritize their need for reliability and maintainability improvement. The results presented in
Paper III identified the weakest FC type of the system and indicated to what type of improvement
that need to enhance the availability.
The reliability, maintainability, sharing power, capacity factor, similarity, and service hours were
used as criteria for the MCDM. A multi-choice questionnaire was designed for the assignment of
weights for the criteria, and a matrix was built accordingly for importance judgment. In order to
support the experts while giving their answers, a comparative chart was used to visualize the
differences between the performance measures and provide a platform for comparing the current
status of all the converter types, see Figure 6.8. Further details on the experts’ opinions and the
MCDM utilised can be found in Paper III.
48
a
b
Figure 6.8: Comparative availability-maintainability chart (a) and comparative reliabilitymaintainability chart (b).
Table 6.4 shows the rankings of the FC types, based on the results obtained from the AHP-based
MCDM. It is evident that the TGTO has the highest priority for improvement, followed by the
YRLA and Q48.
In addition, a detailed analysis of the TGTO was performed. Figure 6.9 shows the distribution of
the failures of the TGTO converter among all its subsystems. This analysis showed that the least
reliable component is the inverter and found that the snubber capacitor of the inverter has a
dominant effect on the unreliability and unavailability of the inverter subsystem.
Table 6.4: Priority ranking of the FC types.
1
2
3
4
5
6
7
8
9
10
Converter Models
TGTO
YRLA
Q48
Q38
Q24
Cegelec
Areva
Mega I
Mega II
YOQC
49
Weights
0.198
0.166
0.101
0.092
0.081
0.078
0.077
0.075
0.069
0.064
Distribution of No. Of failures (%)
40
35
30
25
20
15
10
5
0
TGTO
subsystems
Figure 6.9: Distribution of the failures of the TGTO converter among its subsystems.
This capacitor is subjected to several types of stress: overvoltage, overheating, overcurrent,
energy, pollution, humidity, radiation, and vibrations. All of these stress factors cause different
failure modes, such as decreased capacitance, short circuits, and open circuits. According to the
results of capacitor autopsies and tests, the most common failure mode in this case is the short
circuit failure mode. There are many stages in the life of a capacitor before it reaches the stage of
ignition and explosion of the bottom bobbin. The snubber capacitor of the inverter contains three
bobbins connecting electrically in parallel. Figure 6.10 shows the results of measurements of the
percentage loss of capacitance (Figure 6.10a) and tan G (Figure 6.10b). tan G is a measure of lossrate of energy. These measurements were performed for six capacitors after 9,000 working hours
in TGTO converters. According to these measurements, it is evident that, in all the capacitors, the
bottom bobbin performs worse than the other two.
180
8
160
7
14
140
6
120
12
5
10
100
tan G
200
9
% loss of C
10
4
8
80
3
60
2
40
1
20
0
1 2 34 5 6
Bottom bobbin
1 2 34 56
Middle bobbin
0
12 345 6
Top bobbin
a
12 3 4 5 6
Bottom bobbin
12 3 4 5 6
Middle bobbin
12 3 4 5 6
Top bobbin
b
Figure 6.10: Comparison of the three bobbins of 6 capacitors concerning their % loss of
capacitance in (a) and tan G in (b).
If any one of the above-mentioned stress factors or a combination of them exceeds the threshold,
this will lead to an increase in the degradation, a greater loss of capacitance, and finally
breakdown. The capacitor autopsies showed that a combination of overvoltage and overheating
50
occurs. Moreover, Figure 6.11 shows the temperature distribution over different locations in the
snubber capacitor under operational conditions, and indicates that there is a high temperature in
the bottom terminal. Two measures have to be taken to decrease the failure rate of the snubber
capacitor: derating of the capacitor voltage and cooling of the bottom bus bar of the capacitor. A
reduction of the two main factors will reduce the short circuit failure mode, namely overvoltage
and overheating, and then definitely will reduce the self-healing power within wounded film
capacitors. In this case the most effective solution is temperature reduction, and bus bar cooling
will result in the failure rate of the bottom bobbin being reduced to the failure rate levels of the
middle and top bobbin. Consequently, this improvement will enhance the overall availability of
the TGTO converter.
The end
terminals
Figure 6.11: Result of a temperature measurement of the capacitor using an infrared camera.
In the station level, reducing the loss of load is addressed by identifying the stations that have a
shortage of reserve power capacity. In the case of the TPSS, the load is only the train and can be
measured in terms of the train traffic density. In the present study, a practical approach was
developed for the reliability model of the traction power capacity, and this approach depends on
the traffic density. One main benefit of the developed model is that it can work when detailed
electrical information on both the load and the FC models is missing. One needs to focus more
attention on those stations which have a loss of capacity and increase their maintenance activities.
On the other hand, in the case of the stations which have a shortage of reserve capacity, one needs
to install more FC units or redistribute the rotary converters. Figure 6.4 shows the proposed
model for reliability evaluation of the traction power capacity, which depends on measurements
of the following values: the state probability (SP) and risk values, which represent the loss of
capacity, and the value of the power-traffic factor (PTF), which indicates the shortage of reserve
capacity. The SP represents the probability of the whole station capacity being available, while
the risk represents the probability of the whole station capacity being unavailable. The PTF can
be used for comparisons between the power capacity and the loads from traffic expected to affect
the converter stations. This measure is employed to evaluate whether or not the reserve capacity
51
is sufficient to cover the demanded traffic when there is a lack of information from the traditional
load model. By using the developed PTF measure for railway systems, one does not need to
compare the loss of capacity with the load profile using the same electrical unit (MVA).
Table 6.5 shows the PTF, SP, risk and delay for some of the stations that have the highest traffic
delay with another station (Älvsjö) for comparison. The PTF value refers to the reserve capacity,
the SP and risk refer to the loss of capacity, and the traffic delay refers to the loss of load due to
the shortage of power conversion capacity. The Malmö and Sköldinge stations have almost the
same PTF measure, 43.3 and 43.2, respectively, which means that the reserve capacity of these
two stations is almost the same. However, the loss of load (traffic delay) for these stations
exhibits a big difference: 60 and 28 h for Malmö and Sköldinge, respectively. When there is a big
difference in the loss of load between stations that have the same reserve capacity, this means that
there is also a difference in the loss of capacity. The risk values presented in Table 6.5 indicate
that the risk in Malmö is six times greater than that in Sköldinge, and this, together with the SP
values shown in the same table, indicates that the Malmö station has a greater loss of capacity
than the Sköldinge station.
Moreover, it is evident from Table 6.5 that the Älvsjö station has acceptable PTF and SP values,
and has a low risk, as a result of which the traffic delay is low compared to the other stations. The
Hässleholm station has a good PTF value, meaning a good reserve capacity, while the SP and risk
indicate a higher loss of capacity, so that the traffic delay for this station is very high, 63 h. It
should be concluded that the SP and risk are the most important factors for identifying where
maintenance activities are needed.
Table 6.5: The values of the PTF, SP, traffic delay and risk for some FC stations.
Station
Reserve capacity
Loss of capacity
Loss of load
PTF
SP
Älvsjö
82.1
0.94660257
<1
0.35
Malmö
43.3
0.90332227
6
60
Sköldinge
43.2
0.98442484
<1
28
Falköping
49.5
0.98039987
27
44
Olskroken
75
0.86549942
<1
87
Hässleholm
90
0.56525609
861
63
Traffic delay (h)
Risk
Paper IV presents research conducted to identify the correlation between the traffic delays and the
unavailability of FC stations. It is evident in Figure 6.12 that the correlation coefficient for the
correlation between the traffic delay and the unavailability is 0.159, while that for the correlation
between the traffic delay and the PTF is 0.694. Thus, the overall traction power capacity in the
52
Swedish TPSS does not include enough reserve capacity to handle the demand when a converter
unit is not in service due to outage or maintenance. Determination of the amount of reserve
capacity required to ensure an adequate supply is an important aspect of power system planning
and operation. The installed capacity should equal the expected maximum demand plus a fixed
percentage of the expected maximum demand. The capacity must be sufficient to provide the
load in the event of forced outages (planned or scheduled) and load growth requirements in
excess of the estimates.
0.7
100
U = 0.159
U = 0.694
80
0.5
0.4
PTF
Unavailability
0.6
0.3
60
40
0.2
20
0.1
0.0
0.0
5.0
10.0
0
15.0
0.0
Traffic delay
a
5.0
10.0
15.0
Traffic delay
b
Figure 6.12: (a) The correlation of the traffic delay with the unavailability and (b) with the
reserve capacity.
The approach adopted in the study for reducing the loss of load is implemented on two levels, the
FC type level and the station level. On the type level, the loss of load is reduced by addressing the
loss of capacity or capacity outage by increasing the availability performance of the lower
performing FC types. On the station level, the reliability evaluation of the power capacity for
each station can identify what each station needs for protection against loss of load. The SP and
risk are valuable factors for prioritizing the need for maintenance activities for reducing the loss
of capacity, while the PTF measure can be employed to evaluate whether or not the reserve
capacity is sufficient to cover the demanded traffic.
53
54
7.
CHAPTER 7
RESEARCH CONCLUSIONS
The research presented in this thesis has focused on evaluation and improvement of the reliability and
availability of a converter-fed electrified railway system. The conclusions drawn from the study are
summarised below.
x The outage data need to be included in the operation and maintenance data reporting system,
and information such as the unit state, time designations, and power/energy terms must be
recorded in the system. The study found that these data could be established by using a
computer programing code to extract the missing information from electrical measurements.
The purpose of the extraction process was to meet the requirements for outage data for
performance evaluation.
x The three-state reliability model is an appropriate reliability model for the frequency converter
(FC), for both performance assessment and reliability evaluation of the traction power
capacity. The four-state reliability model for FCs was modified in this study into a three-state
model. The reason for this modification is that there was a lack of information regarding the
separate forced-out states, in addition to the fact that the developed model for reliability
evaluation of the traction power capacity can be implemented by using the general forced
outage rate (FOR).
x Reliability evaluation of the power capacity involves a comparison between the power
generating capacity and the load profile in order to find the reason for the loss of load. In the
TPSS, the load is demanded according to the traffic density, and therefore the traffic delay was
assumed in the study to refer to the loss of load. The term “capacity outage” refers to the loss
of capacity. The study developed the PTF measure for comparisons between the power
capacity and the traffic density. This measure can be employed to evaluate whether or not the
reserve capacity is sufficient to cover the demanded traffic.
55
x The stations that have a higher loss of load, such as Olskroken, do not belong to those which
have the lowest performance measures. It can be concluded that a low availability
performance of a station may or may not refer to a loss of load, and vice versa. The reason is
that a loss of load depends on the power capacity and the load level.
x In the Swedish electrified railway system, the main reason for the loss of load of stations such
as the Malmö and Falköping stations is a shortage of reserve capacity. On the other hand, there
are some stations which have a high loss of load due to a large loss of capacity, meaning that
they have a high outage rate; an example of such a station is Hässleholm. In fact, through
appropriate maintenance the FOR can be reasonably reduced, and when prioritising the need
for maintenance, infrastructure managers need to consider the state probability (SP) and risk
for the stations which have a higher loss of capacity.
x Using both the MCF and the performance measures together provides a complete picture of
the performance of FCs. The performance measures have the ability to reflect the capacity
outage, but cannot indicate the trend and future expectations. The MCF, on the other hand,
provides information on the trends, revealing whether the reliability is improving,
deteriorating, or staying normal against the usage intensity.
x The snubber capacitor has a dominant effect on the reliability of the TGTO converter type.
Since a higher reliability cannot be achieved just by reliability design, one must also possess
knowledge and understanding of possible failures and their mechanisms. The most common
failure mode of the capacitor of the TGTO is a short circuit caused by overvoltage and
overheating. Cooling the bottom bus bar of the capacitor can raise the reliability level of the
bottom bobbin to that of the top and middle bobbins.
56
8.
CHAPTER 8
RESEARCH CONTRIBUTIONS
AND FURTHER WORK
8.1.
Research Contributions
The scientific contribution of this thesis is an expansion of the body of knowledge about
reliability and availability analysis in the field of converter-fed electrified railway systems. Some
of the specific research contributions are summarised below.
x The IEEE four-state reliability model has been modified to a three-state model which can
function as an appropriate model for frequency converters as intermittently operating units, for
the purpose of performance assessment and reliability evaluation of the traction power
capacity (Paper I).
x A method based on Matlab programing code has been devised to extract the outage data from
the available field data, which consist of failure data and electrical measurements (Paper I).
x A method using both the MCF and performance measures together has been devised for
evaluation and comparison of the RAM performance of ten types of frequency converters
(Paper II).
x Ten frequency converter types have been subjected to evaluation and selection by using
different methods for determining the priority of their need for improvements and by unifying
the overall assessment. Moreover, a comparative chart has been developed to provide a
tangible representation of the current status of different frequency converter types (Paper III).
x A model for reliability evaluation of the traction power capacity has been developed which
can be used to identify the reasons for loss of load in electrified railway networks. Using this
model, one can identify where resources should be invested, and one can determine whether
one should install more units or perform more maintenance activities to enhance the overall
availability (Paper IV).
57
8.2.
Further Work
During the progress of the present study, several interesting research topics have come to our
attention. However, it has not been possible to pursue all of these within the research framework
of this thesis. Hence, in this section some of these topics are proposed as a focus for further
research.
x The study shows that different FC stations need different actions for reducing their loss of
load. The stations that have a higher traffic delay due to a loss of capacity need more
maintenance actions for their FC units, while those stations which have a shortage of reserve
capacity need to have more units installed. Moreover, the results for the PTF show that there is
a problem in distributing the power capacity according to the load density, and to solve this
problem, some rotary converters need to be redistributed. In this connection an optimization
study is needed to select the required actions, taking into consideration the power transfer
among stations, the catenary type, and the distance between stations.
x The demand factor can be used to estimate the hours spent in the “forced-out, needed” state
from the hours in the overall forced-outage state. Many methods have been used in the
literature for estimating the FORd of generating units. Research should be conducted to find a
better way to estimate the FORd for frequency converters in electrified railway applications.
Such research would facilitate the reliability evaluation of frequency converter units and the
traction power capacity. More measurements would be needed to perform this research for
different stations.
x There are many levels of outage data, depending on what the infrastructure managers need
from their data reporting systems. Monitoring the maintenance performance or the available
power capacity (the derating issue), etc. needs different recorded data from those required for
evaluation of the reliability and availability performance. Research should be conducted to
determine the amount of additional information that needs to be recorded to create outage data
on different levels, and comparisons should be made with the existing data that have already
been recorded in 0felia and GELD. A technical study should focus on integration of the
existing recorded data and the new required information, with a view to achieving the goal of
installing the minimum amount of required hardware.
58
9.
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65
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International Journal of Power and Energy Systems, Vol. 34, No. 4, 2014
EVALUATION AND SELECTION FOR
AVAILABILITY IMPROVEMENT OF
FREQUENCY CONVERTERS IN
ELECTRIFIED RAILWAY
Yasser A. Mahmood,∗,∗∗ Alireza Ahmadi,∗∗ and Ajit K. Verma∗∗∗
Sweden, the low-frequency single-phase power is supplied
by frequency converters (FCs), which transfer energy from
the three-phase public grid to the single-phase traction
grid. FCs are an important part of traction power supply
systems (TPSSs), which provide adequate traction power
to the electrified rail networks. Therefore, any major
fault in FCs or any other part of TPSSs will cause operational problems for trains, thus resulting in traffic delays
or cancellations. Currently, the electrified railway sector
is imposing high demands on TPSSs. An analysis of the
annual delays caused by the Swedish TPSS shows that
the number of delay hours due to FC outages exceeded
60 h in 2010 [1]. Operational interruptions due to the unavailability of FCs have significant economic consequences.
Therefore, railway infrastructure managers are obliged to
enhance the availability performance of FCs while simultaneously optimizing their budget and allocation of resources. The aim of this study is to rank FC types on
the basis of the priority of needs for improving availability
performance.
To achieve maximum availability performance and
reduce the risk of power unavailability, resources must
be allocated properly to the appropriate units to enact
necessary improvements. Therefore, FC types must be
prioritized according to predefined importance criteria to
determine their criticality/priority and to identify the types
that must be focussed upon to achieve enhanced availability performance.
Identification of critical units has been studied in numerous fields and especially in the field of electrical power
systems. Setreus et al. [2] proposed component importance indexes to quantify and rank transmission system
components depending on their importance for system reliability under different load scenarios. They ranked each
component in accordance with three separate importance
indexes. Chen et al. [3] used fault tree analysis (FTA)
to integrate the reliability of individual components into
a measure of overall system reliability through quantitative evaluation. Their study also identified the critical
components of TPSS using minimum cut sets and sensitivity analysis. The authors in the previous work [4] used
Abstract
Railway traction power supply systems that work at low frequencies
contain frequency converters (FCs) that convert the adequate power
from a public grid to a traction grid.
Availability and system
importance analysis is a key aspect of the overall system performance. It identifies the weakest areas of a system and indicates
where resources must be invested to achieve maximum improvement.
Availability analysis of the FC types is crucial to reduce outages
of traction power that lead to traffic delays and cancellations due
to unavailability of FCs.
The aim of this study is to rank FC
types on the basis of priority of needs for reliability and maintainability improvement so that the availability can be enhanced. The
proposed approach involves collection of outage data by calculating
performance measures, and then these measures are compared via a
comparative chart. Thereafter, opinions of experts are collected and
aggregated through linguistic variables, and the FC types are then
identified using analytic hierarchy process. Reliability, availability
and maintainability have been used as the criteria to identify the
FC types. In addition to power sharing, capacity and service factors
are considered to be productivity measures. Probabilistic performance indexes are used to measure availability, maintainability and
productivity, whereas the mean number of outages is used as a
reliability index.
Key Words
RAM analysis, frequency converter, traction power supply system,
availability improvement, multi-criteria decision-making, analytic
hierarchy process
1. Introduction
In many countries, low-frequency single-phase voltage is
used to provide electric power to railway systems. In
∗
Department of Electrical Engineering, University of Mosul,
Mosul, Iraq; e-mail: [email protected]
Department of Operation and Maintenance, Lule˚
a University
of Technology, Lule˚
a, Sweden; e-mail: [email protected]
∗∗∗ Department of ATOM, Stord/Haugesund University College,
Haugesund, Norway; e-mail: [email protected]
Recommended by Prof. A. Domijan
(DOI: 10.2316/Journal.203.2014.4.203-0108)
∗∗
1
probabilistic performance measures for reliability, availability and productivity [5] to study the ranking of FC
types through a comparative chart.
2. Availability Improvement
To achieve a high level of operational availability, the
following three criteria must be addressed: reliability,
maintainability and supportability. The proximity and
availability of maintenance personnel, complexity of diagnosis and repair and availability of spare parts significantly
influence the operational availability. In this section, we
will consider reliability and maintainability issues as means
of improving operational availability.
The priorities of improvement requirements depend
on other factors. For example, an FC with a higher
sharing power and productivity capability must be prioritized relative to other units for improvement consideration. To this end, this study considers sharing power,
capacity factor (CF) and service factor (SF) as productivity criteria and reliability, availability and maintainability (RAM) as RAM-related criteria. The probabilistic indexes are used to measure FC availability, maintainability and productivity, whereas the mean of the
number of outages is used as the reliability index. It
should be noted that in reality, each criterion contributes
to the priority of needs for availability improvement to
a different degree. For instance, the FC that has maximum sharing power must be a high priority for improvement when compared with those that have lower sharing
power.
2.1 Reliability Improvement
Reliability is a characteristic of an item expressed by the
probability that the item will perform its required function
under given conditions within a specific time interval. The
reliability function R(t) gives the probability that the item
operates without failure in (0, t] given item new at t = 0,
R(t) = P r{up in at (0, t] | new at t = 0} [10]. To reduce
the number of failures and increase maintenance intervals,
the reliability of electrical equipment must be improved.
Reliability analysis identifies the weaknesses in critical subsystems and components and problem areas in design that
require attention from an operational capability viewpoint
and distinguishes those that are less critical. Reliability
analysis can be applied in engineering design to determine
if it is more effective to rely on redundant systems or
upgrade the reliability of a primary unit to achieve the
required level of operational capability. A higher reliability
cannot be achieved just by quality control and reliability
design. In fact, knowledge and understanding of possible failures and their mechanisms is necessary. Numerous
methods, including FTA, ETA, reliability block diagrams
and Markov method [11], are used to perform reliability
analysis of converters. In addition, failure mode and effects
analysis (FMEA) is an important technique to identify and
eliminate known or potential failures to enhance system
reliability [12]. The reliability improvement index is another method used to rank the sensitivity of components
to prevent component failures and is calculated using fuzzy
logic [13].
To assign weights to the criteria, the experiences of
field experts are used as an effective database. However,
experts have to face numerous and conflicting evaluations
when comparing the criteria. They must take into account
numerous tangible and intangible criteria, which represent
the primary complexity of the problem.
Therefore,
the
multi-criteria
decision-making
(MCDM) approach was proposed previously and has
gained impetus in the field of power systems to provide
a comprehensive evaluation method and overall assessment of system performance [6], [7]. On the other hand,
analytic hierarchy process (AHP) is used as a tool of
MCDM for improving availability through identification of
the most critical components of electrical power systems
and prioritization in maintenance scheduling and strategy
[8]. Rietz and Suryanarayanan [9] reviewed this type of
AHP applications using experts’ judgements as a tool of
MCDM, which has been successfully applied in electric
power systems for the design and operation of electric
power grids including selecting generating units, determining distributed generator, forecasting loads, integrated
resource planning, load shedding and optimizing cost.
Within this frame, the opinions of the experts have been
collected as linguistic variables and aggregated using fuzzy
methodology and then converted from the linguistic scale
to a crisp value. Consequently, the AHP methodology
is used in this study as a tool of MCDM to rank FC
types on the basis of experts’ judgements and performance
criteria.
2.2 Maintainability Improvement
Maintainability is defined by the International Electrotechnical Commission as “the ability of an item under given
conditions of use to be retained in, or restored to, a state
in which it can perform a required function, when maintenance is performed under given conditions and using
stated procedures and resources” [10]. The amount of time
required to complete a given repair will be the sum of the
time required to detect a failure, summon maintenance
personnel to the site, diagnose the problem, obtain and
install spare parts, test the repair and reactivate the inverter. Forced outage hour (FOH) or forced downtime is
an indicator of maintainability performance of any system.
Figure 1 shows the primary components of operational
time and the reliability and maintainability improvement
areas.
The rest of the paper is organized as follows. Section 2
presents the factors affecting the availability improvement,
and Section 3 discusses the methodology used. A case study
is presented in Section 4. The probabilistic performance
measures and the ranking of FC types by using these
performance measures are introduced in Sections 5 and
6, respectively. The ranking of FC types by comparative
chart and by using AHP are given in Sections 7 and 8,
respectively. Finally, the conclusions are presented in
Section 9.
2
Figure 1. Schematic of the main unit states and residence times of converters showing areas for reliability and maintainability
improvements.
The unplanned FOHs represent the active repair time
and the delay time associated with repair action. In fact,
the delay is caused by the lack of supportability, whereas
the active repair time is due to maintainability and diagnosability. The complexity of the diagnosis and repair
process has a significant impact on maintainability. Therefore, a recommended maintainability must be achieved in
complex equipment and systems by considering the following aspects: 1. Fault detection and isolation, including
functional testing after repair. 2. Fault diagnosis. 3.
Fault correction to restore the ability of the faulty item to
perform a required function [14].
The diagnostic model is developed by using the information obtained from the FMEA and FTA. The FMEA
includes the functions of the components, failure rate of
each component in all failure modes, effects of the failures and failure indications corresponding to the failure of
each component. The FTA focuses on particular failures
and failure indications corresponding to each component
in the system. The information from the FMEA and FTA
is combined to represent the failure rates and indications
associated with a system [10].
Figure 2. The steps of proposed approach of FC selection
for improvement.
The standard provides a methodology for interpreting the
electric-generating unit performance of different systems
and comparing the different systems. The following indexes are used in this study: SF, availability factor (AF),
CF, forced outage factor (FOF) and forced outage rate
(FOR) [15], [16].
However, there are some limitations of using individual
indexes such as FOF and FOR as performance measures
because they directly depend on downtime, and there is
no representation of the number of outages. Moreover, the
disadvantage of using FOF is that there is no information
about the SFs such as FOR, whereas the disadvantages of
both are that there is no information about the number
of outages and loading. Therefore, collective performance
measure indexes, such as performance factor (PF), are used
to determine the influence of number of outages, FOH, SF
and CF.
Another issue concerning the accurate measurement
of reliability, based on the definition of reliability, is that
the number of failures and the uptime must be taken into
account, whereas the FOF and FOR indexes are based on
the FOH (downtime). Hence the number of outages has
been used as the reliability measure in this study. The
differences in converter types with respect to structure and
operation mechanism lead to differences in the maintenance
time. The MNoOs has been used as an indicator of
3. Methodology and Study Approach
The probabilistic performance indexes are used to measure
FC availability, maintainability and productivity, whereas
the mean number of outages (MNoOs) is used as the reliability index. It should be noted that in reality, each of these
criteria contributes to a different extent to the priority of
needs for availability improvement. To assign weights to
the criteria, the experiences of field experts are used as an
effective database for estimation. To clearly understand
the FC types and their performance differences, a comparative chart is proposed. The opinions of experts have
been aggregated using fuzzy methodology to convert the
linguistic scale to a crisp value. Consequently, the AHP
methodology is employed in this study to rank the performance criteria. The steps of the proposed methodology
are shown in Fig. 2.
3.1 Performance Measures for Generating Unit
The IEEE 762 standard provides terminology and probabilistic indexes to measure and report the reliability,
availability and productivity of an electrical power unit.
3
where A is the pair-wise comparison matrix for elements
1, 2 and n representing the relative importance of criteria
with respect to a goal. The eigenvector method is used
to calculate the relative weights in AHP. Thereafter, the
relative weights (wi ) of matrix A are obtained by solving
the following:
reliability, whereas the AF and FOH have been used for
availability and maintainability, respectively.
For comparing these measures, a comparative chart has
been used. The comparative chart is a very powerful tool
that provides a tangible insight with a different position
for each FC type and a very simple visual display of
comparison, and it is a basic criterion for ratings. In
addition, the improvement requirements of each type can
be directly viewed and derived. However, the disadvantage
of this chart is that it is impossible to aggregate all the
criteria and to unweigh them [4].
wi =
m
a
¯ij (i = 1, 2, . . . ., m)
(1)
j=1
n
3.2 Analytic Hierarchy Process
W = (w1 , w1 , . . . wn ,)
(2)
wi = 1, (i = 1, 2, . . . ., m)
(3)
i=1
Introduced by Saaty [17], the AHP is a theory of measurement through pair-wise comparisons and relies on the
judgements of experts to derive priority scales to arrive at a
scale of preferences amongst a set of alternatives. The AHP
is implemented by reducing complex decisions to a series
of simple comparisons and rankings and then synthesizing
the results through quantifiable and/or intangible criteria.
The AHP employs pair-wise comparison in which experts
compare the importance of two factors on a relatively subjective scale. Therefore, a questionnaire is necessary for
importance judgement matrix depending on the relative
importance given by the experts [18], [19]. This questionnaire is represented by linguistic and vague patterns and
can be easily filled by experts. Therefore, a much better
representation of this linguistics can be developed as a data
set and then refined using the evaluation methods of fuzzy
set theory [20]. Another important consideration in AHP
is the notion of consistency. Consistency is the degree
to which the perceived relationship is maintained between
the elements in the pair-wise comparisons. It is important because comparisons lacking consistency may indicate
that the respondents did not understand the differences in
the choices presented or were unable to accurately assess
the relative importance of the elements being compared.
On the other hand, lack of information about the criteria
being compared or lack of concentration during the judgement process can also cause inconsistency. Application of
the AHP methodology in a decision problem involves four
steps [21]:
1. Structuring the decision hierarchy (goal, criteria and
decision alternatives).
2. Collecting input data, depicted by matrices of pairwise comparisons, of decision elements.
3. Using the Eigen value method to estimate the relative
weights of the decision elements.
4. Aggregating the relative weights of decision elements
to arrive at a set of ratings for alternatives.
The hierarchy of the decision problem consists of a goal
(G), a set of criteria Cj (j = 1, 2, . . . , M ) and a set of
alternatives Ai (i = 1, 2, . . . , N ). In the AHP, initially
a sequence of M × (M − 1)/2 pair-wise comparisons of
criteria are performed with respect to a goal using Saaty’s
original nine-point scale. The numerical values of this
scale and the corresponding levels of importance are equal,
moderately strongly, very strong and extremely strong
[19]. In this manner, a judgement matrix A is created,
Finally, the local weights of the attributes and the local
weights of the alternatives with respect to the attributes
are combined according to AHP to obtain the total weight
and the overall rating of the alternatives as in (4):
Wi =
n
a
wij
c wij (i = 1, 2, . . . ., n)
(4)
i=1
a
is the
where Wi is the total weight of alternative i, wij
c
local weight of alternative i with respect to attribute j, wij
the local weight of criterion j, m and n denote the number
of criteria and alternatives, respectively.
The disadvantage of AHP is that it does not take into
account the uncertainty associated with the mapping of
human judgement to a number for criteria. Experts do
not have to express how many times an attribute is more
important. They express their opinion through simple linguistic judgements via a questionnaire because it is easy to
understand and faster to fill; thus, the resulting weights are
more accurate [22]. Therefore, fuzzy methodology judgements can consider the uncertainty and vagueness of the
subjective perception [13]. The opinions of different experts must be aggregated. Fuzzy AHP can be implemented
in two different ways: by supporting the whole process till
final prioritization or by determining only the importance
weights for criteria [22]. The second method will be followed here because there is no uncertainty and vagueness
in the input of the alternative side because the results of
performance measures are calculated earlier owing to their
exact values. Moreover, linguistic variables are used only
for the weighting of criteria by experts.
4. Case Study
4.1 Traction Frequency Converters
Ten different types of static and rotary FCs are available.
Rotary FCs have been traditionally used in Sweden since
1915. The rotary type FC is a synchronous–synchronous
motor-generator set, and it is available in Sweden in different types such as Q24, Q38 and Q48 with a rated power
reach of 3.2, 5.8 and 10 MVA, respectively. All the three
types employ the same construction, with the two primary components being a motor and a generator. Static
FC technology has emerged as an alternative to rotary
4
Table 1
Summary of Basic Information of all FC Types Considered in this Study
Frequency
Converter
Type
Rotary
Q24
11
3.2
Q38
45
5.8
261
23
Q48
20
10
200
11
YOQO
16
13–15
234
7
Cyclo-convertor
YRLA
3
8.2
1
PWM-Thy.-two level
TGTO
12
14
168
6
PWM-GTO-two level
Static
No. of
Capacity
Total Capacity Number of Structure
Converters Power (MVA)
(MVA)
Stations
35.2
24.6
8
Mega I
14
15
210
7
Mega II
4
6.7
27
2
Cegelec
2
15
30
1
Areva
7
15
105
4
equipment and offers a significant improvement in energy
efficiency. In Sweden, static FCs have been used for either replacing old rotary converters or in new installations.
The seven types of static converters, which differ by power
capacity, circuit topology and manufacturer, considered in
this study are YOQC, YRLA, TGTO, Mega I, Mega II,
Cegelec and Areva, as shown in Table 1. The YOQC is a
thyristor cyclo-converter. The primary components of the
other types are a rectifier and a pulse width modulation
(PWM) inverter. The YRLA type has a thyristor inverter,
and TGTO, Mega I and Mega II have a two-level GTO
inverter. The Cegelec and Areva have a three-level GTO
inverter. Moreover, TGTO, Mega I, and Mega II are similar in structure but were manufactured in different years,
and Cegelec and Areva have the same construction but are
produced by different manufacturers [1].
Motor-generator
PWM-GTO-three level
to articulate, clarify, define and verify whether the most
important requirement metrics have been met [12]. RAM
analysis may include preventable vulnerabilities within the
system. Therefore, to rank converter types, RAM criteria have been selected and acquire a priority within the
selected criteria.
Sharing Power : The maximum sharing power
achieved from the power capacity of a given type and the
number of converter units of the type have a significant
impact on ranking. Hence, the FC that has a greater
sharing power demands more attention for improvement.
CF : This represents the percentage of actual power
relative to the nominal power converted from each type,
and it represents the productivity and the loading factor.
Similarity : Many converter types have some similarities in their design, structure and/or mechanism of operation though they may have different power capacities
or may be produced by different manufacturers. It is important to include this factor in the identification process.
Similarity is a very important factor for those types having
the same failure mode within the same component to focus
the root cause study for improvement.
SF : It is considered as an indicator of the age of a unit
and represents the amount of time a converter spends on
converting the power to the TPSS over a certain period [16].
It should be noted that under loaded or unloaded conditions, the converters in an FC station are connected and
disconnected with a so-called start and stop automation.
Namely, if the converters in an FC station are loaded below
a predefined threshold, one of them is disconnected depending on the number of trains covered within the lines.
Conversely, if the converters in the FC station are loaded
above a different predefined threshold another converter is
connected [23]. Therefore, CF and SF can not only represent loading and SFs but also are an indicator of demand
factor.
4.2 Selection Criteria
To rank the priorities of needs for improving the reliability
and maintainability of FCs, a set of evaluation criteria that
will adequately assess the effectiveness and efficiency of the
improvement strategy must be formulated to improve the
overall availability of converters. Moreover, these assessments require knowledge of different factors that indicate
the strengths and preferabilities of strategies depending on
the associated evaluation criteria. The evaluation criteria
considered in this study include RAM, sharing power, CF,
distribution (within stations), and similarity with other
types and position, which are described below.
RAM : Reliability, availability and maintainability
performance are the most important factors that must
be taken into consideration during the planning, design,
operation and maintenance of the facilities under study.
RAM measures are essential to define, control, maintain
and improve the performance of a component over time.
RAM analysis provides systems engineers with a method
5
Table 2
Linguistic Terms used in the Pair-Wise Comparison Questionnaire of this Study
Further, l, m and u are considered to be the lower, the
mean and the upper bounds of the membership function,
respectively [8], [22].
4.3 Data Collection
4.3.1 Collection of Empirical Data
5. Performance Measures for FCs
All the empirical data related to the performance of
the converters used in this study were gathered from
two databases of the Swedish Transport Administration
(Trafikverket): 0Felia1 and GELD2 . These historical data
cover 76 rotary and 58 static converters that are distributed
among 46 FC stations in the Swedish railway system, and
the data cover the period between January 2007 and June
2010. Mahmood et al. [16], [24] analysed these databases
and suggested an approach, on the basis of extant data,
for generating missing data to fulfil the criteria needed
to calculate the essential probabilistic indexes provided by
the IEEE 762 standard.
The TPSS is similar to a public grid power system in that
it utilizes an FC that is similar to a generator unit in power
systems, with traction transmission lines and catenary
systems as constituents of the distribution system. To
this end, the IEEE 762 standard provides a methodology
for interpreting the electric-generating unit performance
data of different systems and comparing different systems.
Table 3 shows some of the IEEE 762 probabilistic indexes
that are used to measure the performance of FCs. The SF
represents the length of usage and is considered an indicator
of RAM [25], whereas the AF is the ratio of the amount of
time the converter is able to convert traction power over a
certain period to the amount of time in the period. The AF
parameter has been suggested as a means of measuring the
RAM performance of power plants and is usually evaluated
monthly for comparing different generating systems [15].
The CF is the ratio of the actual energy conversion to the
maximum energy conversion possible, and it is one factors
used to measure the productivity of a power production
facility and to compare different facilities. The percentage
of loading for each FC type must also be evaluated [16],
[26]. According to Mahmood et al. [16], the best measures
for reliability and maintainability are the MNoOs and
FOHs, respectively. The FOF and FOR are performance
indexes, and have been used to measure reliability because
they tend to be somewhat independent of service duty.
Moreover, the FOF can be directly sub-divided into its
contributing factors [5].
4.3.2 Collection of Judgements from Experts
A questionnaire was created to collect the opinions of experts on the weights of the ranking criteria (see Table 2).
For pair-wise comparison of importance between two criteria, the following question was asked: “Which one of these
two factors is important and how much is more important
than other?”.
The experts offered their opinions on the basis of their
knowledge and experience in the multiple-choice questionnaire that formed the basis of the pair-wise comparison
matrix for the given criteria. The verbal and qualitative responses were then quantified and translated into a
quantitative value/score using a discrete nine-point scale
[18]. Because of the inability of AHP to adequately deal
with uncertainty and imprecision associated with the mapping of the decision makers’ perception to crisp numbers,
the fuzzy sets theory is employed in the problem. Fuzzy
sets are introduced in the pair-wise comparison of AHP
where fuzzy ratio scales are used to indicate the relative
importance of the criteria. The judgements, uncertainty
and imprecision of the specialists who are involved in the
decision-making problem can be easily controlled by using them, thus leading to more reliable and accurate final
results [8]. Because of their popularity, triangular fuzzy
numbers are used to fuzzify the traditional AHP. A triangular fuzzy number is often represented as a triplex of
(l, m, u) and can be described as a membership function.
1
2
6. Ranking by Performance Measure
The indexes for generating units involve detailed performance evaluation of converters. However, infrastructure
managers must also assess the total FC performance to
examine if there is any negative trend in their performance.
Therefore, managers need a simple method to detect low
performance, and FOF and FOR are performance indexes
that provide an appraisal of the outage time. However, the
difference between them is related to the reserve shutdown
hour, which is not considered in the FOR index calculation. On the other hand, it is useful to aggregate many of
the indexes by a collective measuring factor to identify the
status of FC performance over a specific period of time and
to conduct daily audits. Therefore, we introduce a ranking
A fault-reporting database that gathers data covering all
significant infrastructure system events.
A power management system which, amongst other functions,
handles electrical readings from field measurements.
6
Table 3
Performance Measures of FC Types
FC Types
Performance Measures
Q24 Q38 Q48 YOQC YRLA TGTO Mega I Mega II Areva Cegelec
AH
AF (%) = P
H ∗ 100
98.7 98.7 98.1
99.17
98.77
97.77
98.64
99.60 98.23
98.22
42.4 62.1 51.0
62.23
80.89
74.97
61.44
70.71 63.28
66.58
43
43
40
38
SF (%) =
SH
PH
∗ 100
T EC
CF (%) = IC∗P
H ∗ 100
60
55
45
1.2
1.2
1.8
0.82
1.22
2.22
1.35
6.1
2.4
4.2
1.962
1.517
3.09
2.496
FOH (h)
256 238 335
151.5
223.3
407.5
247.5
71.5 322.2
325.5
MNoO
4.5
5.68
24.66
14
5
F OF (%) =
F OH
PH
∗ 100
F OH
F OR (%) = F OH
+ SH ∗ 100
4.2
4.8
Table 4
PF for Converter Types
No.
1
2
3
FOF
FOR
PF
TGTO
Q24
TGTO
Q48
Q48
Cegelec TGTO
Q48
Areva
Areva
5
Mega I
Mega I
YRLA
6
Q38
Q38
Cegelec
Q24
YOQC
Mega I
7
Q24
8
YRLA Cegelec
9
YOQC
10
Mega II Mega II Mega II
YRLA
Q38
YOQC
methodology that takes a holistic approach for ranking
FCs depending on their collective performance.
The collective performance measure introduced in this
study is based on the aggregation of the probabilistic
performance indexes introduced in the IEEE 762 standard,
the number of outages and the outage duration as in (5):
P erf ormance F actor (P F ) =
28
0.39
1.76
1.77
0.578 2.999
1.854
8.5
5.42
40
4.5
of the availability of the FC types considered versus their
maintainability. In the figure, the upper right corner represents the condition where both availability and maintainability are high. Similarly, the lower left corner represents
the condition where both availability and maintainability
are low. The curves in the figure represent reference lines
at points equidistant from the origin to facilitate comparison of the FC types. As can be clearly seen from the
figure, although the availability levels of all the FCs are
reasonably high, the TGTO, Q48, Cegelec and Areva types
have the lowest availability level as well as relatively low
maintainability. Figure 3(b) shows a comparison of the
reliability of FC types versus their maintainability. As
shown in Fig. 3(b), theQ48, Cegelec and Areva types have
relatively better reliability in comparison with the other
types except for TGTO. Therefore, it can be concluded
that Q48, Cegelec and Areva types have the least availability because of low maintainability, and the TGTO type
has a low availability performance owing to both low reliability and maintainability. As is evident from Fig. 3(b),
the YRLA type has the lowest reliability performance of
all the types. However, it has relatively higher availability
performance because of relatively higher maintainability.
Therefore, to improve the availability of the YRLA type,
its reliability must be improved. From the results obtained from Figure 3, the TGTO type can be considered to
be the type that has priority for improvement from both
reliability and maintainability viewpoints.
As can be seen in Fig. 3(b), the Cegelec, Q48, Areva,
Q38, Q24, Mega I and YOQC types have similar reliability
levels although the Q38, Q24, Mega I and YOQC types
have relatively higher maintainability. Further, Mega II
has a relatively lower reliability than the above-mentioned
FC types although it has the highest maintainability. It
is obvious from the figures that the TGTO type has the
lowest maintainability, whereas the YRLA type has the
lowest reliability level of all the types.
Table 5 shows the ranking of the FC types according to the priority of improving availability through the
enhancement of reliability or maintainability. It should
be noted that all the criteria have the same importance
weight.
Areva
4
27
AF ∗ SF ∗ CF
F OH ∗ F OR ∗ F OF ∗ M N O
(5)
Table 4 shows the ranking of FOF, FOR and PF for all
the converter types. As can be clearly seen, the results
obtained from the ranking by PF measures are somewhat
similar to those of FOR and FOF ranking.
7. Comparative Chart
To obtain a clear picture of the FC types and their performance differences, a comparative chart is proposed to
visualize the performance measures of all the FCs, as can
be seen in Figs. 3 and 4. Figure 3(a) shows a comparison
7
Figure 3. Distribution of converter types within reliability–maintainability chart in (a) and availability–maintainability chart
in (b).
Table 5
Priority of Improvement based on RAM
No. Converter Type Priority of Improvement
Figure 4. Comparative chart of availability versus sharing
power for all converter types.
Figure 4 shows the maximum sharing power versus
the availability level for each FC type. The upper left
corner of the graph represents a high sharing power with
relatively low availability. It is reasonable to expect higher
availability levels for FCs having high sharing power. In
addition, some of the converter types have a lower number
of converters; therefore, the sharing power tends to be
low (see Table 2). Therefore, we can conclude that if
all the criteria have the same importance weight, FCs
such as Cegelec that has two converters and YRLA with
three converters have less priority for improvement of
availability when compared with YOQC that has a total of
16 converters.
1
TGTO
Maintainability
2
TGTO
Reliability
3
Q48
Maintainability
4
Cegelec
Maintainability
5
Areva
Maintainability
6
Mega I
Maintainability
7
YRLA
Reliability
8
Q24
Maintainability
9
Q38
Maintainability
10
YOQC
–
11
Mega II
–
for each FC type and a very simple visual display of comparison, and this feature is a basic criterion for ratings.
Moreover, it can directly show the improvements needed in
each type. The disadvantages of the chart and performance
measures are the impossibility of aggregating and weighting criteria at the comparison from the ranking viewpoint.
Therefore, the AHP, as a multi-criteria decision maker, can
be used to unify the factors by weighting the criteria that
are used for ranking. The first step in the AHP is the
hierarchy diagram. It is a graphical representation of the
decision problem in which the objective is at the highest
level; the criteria are at the intermediate level and the alternatives are at the lowest level. The hierarchy developed
for this study with six criteria (C1–C6) and 10 alternatives
(A1–A10) is shown in Fig. 5.
8. Ranking by AHP
The comparative chart is a powerful tool because it provides a tangible representation with a different position
8
Figure 5. Decision hierarchy of evaluation criteria for FC type ranking.
Table 6
The 6 × 6 Matrix for Criteria Pair-Wise Comparison
Reliability Maintainability
Reliability
–
CF
Sharing Power
SF
Similarity
4.3
4.82
5.73
4.32
3.72
Maintainability
0.232
–
4.95
5.11
4.59
5.73
CF
0.233
0.202
–
2.9
1.79
3.14
Sharing power
0.207
0.196
0.345
–
2.29
3.7
SF
0.175
0.218
0.559
0.437
–
3.51
Similarity
0.231
0.175
0.318
0.270
0.285
–
Table 7
Selected Criteria of FC Type
Table 8
Priority Ranking of FC Types with Respect to Goal
Criteria
Weights of the Criteria
1 Reliability
0.437
1
Converter Types Weights
TGTO
0.198
2 Maintainability
0.269
2
YRLA
0.166
3 CF
0.106
3
Q48
0.101
4 Sharing power
0.080
4
Q38
0.092
5 SF
0.067
5
Q24
0.081
6 Similarity
0.041
6
Cegelec
0.078
7
Areva
0.077
8
Mega I
0.075
9
Mega II
0.069
The next step involves the collection of input data for
criteria from experts and for alternatives from the results
of performance measures. The result of the pair-wise
comparison data provided by experts is a 6 × 6 pair-wise
comparison matrix (Table 6), including all the required
pair-wise comparisons of the six attributes.
The aggregated weights of the six criteria are shown
in Table 7 with highest related to reliability and the lowest related to similarity. Thereafter, for each of the six
attributes, the performance input data are used for pairwise comparisons of the 10 FC types for alternatives. The
results of the comparisons of the 10 alternatives based on
these six criteria were based on the performance measures
and the basic information given in Table 3.
10 YOQC
0.064
The relative importance of the decision elements
(weights of criteria) is assessed indirectly from the comparison judgements of the decision process as well as the
performance measures of the alternatives. The values of
the weights and measures are determined from these comparisons and represented in a final decision table (Table 8).
The priority rankings that represent the comprehensive
9
evaluation of what must be improved in all the FC types
are shown in Table 8. It is evident that the first rank that
represents the lowest performance belongs to the TGTO
converter type. It should be noted that even though some
differences exist between the different rankings obtained
using PF, the first position belongs to the TGTO converter
type. It should also be noted that the overall consistency
of this AHP process was less than 0.1 (0.07) because of
using the AHP proposed by Saaty [19].
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[18] A. Ahmadi, S. Gupta, R. Karim, and U. Kumar, Selection of
maintenance strategy for aircraft systems using multi-criteria
decision making methodologies, International Journal of Reliability, Quality and Safety Engineering, 17(03), 2010, 223–243.
[19] T.L. Saaty, Decision making with the analytic hierarchy process, International Journal of Services Sciences, 1(1), 2008,
83–98.
¨
¨
[20] A. Ozda˘
go˘
glu and G. Ozda˘
go˘
glu, Comparison of AHP and
fuzzy AHP for the multi-criteria decision making processes
¨
with linguistic evaluations, Istanbul Ticaret Universitesi
Fen
Bilimleri Dergisi, 6(11), 2007, 65–85.
[21] F. Zahedi, The analytic hierarchy process—A survey of the
method and its applications, Interfaces, 16(4), 1986, 96–108.
[22] G. Locatelli and M. Mancini, A framework for the selection
of the right nuclear power plant, International Journal of
Production Research, 50(17), 2012, 4753–4766.
[23] L. Abrahamsson, Railway power supply models and methods
for long-term investment analysis, Licentiate Thesis, KTH,
Stockholm, 2008.
[24] Y.A. Mahmood, R. Karim, and M. Aljumaili, Assessment of
reliability data for traction frequency converters using IEEE Std
762 – A study at Swedish railway, The 2nd Intern. Workshop
and Congr. on Maintenance, Lule˚
a, Sweden, December 2012,
171.
[25] G.M. Curley, Power plant performance indices in new
market environment: IEEE Standard 762 Working Group
activities and gads database, Power Engineering Society
General Meeting, IEEE, Montreal, Que, 2006, pp. 5–xx.
DOI: 10.1109/PES.2006.1709544.
ˇ
[26] M. Cepin,
Reliability and performance indicators of power
plants, in Assessment of power system reliability, methods and
applications (London: Springer-Verlag, 2011), 197–214.
9. Conclusion
System availability analysis is a key aspect of the overall
system performance. It identifies the weakest areas of a
system and indicates where resources must be invested to
achieve maximum system improvement. This study introduces a new approach to evaluate and rank the performance of power units in TPSSs or other power systems to
select the priority of needs for availability improvement.
This study considers sharing power, CF and SF as productivity criteria and RAM as RAM-related criteria. The
probabilistic performance indexes are used to measure FC
availability, maintainability and productivity, whereas the
MNoOs is used as a reliability index.
To assign the weights to criteria, the experiences of field
experts are used as an effective database for estimation.
A questionnaire has been developed to collect the opinions
of experts. To increase the accuracy of expert evaluation
when answering the questions, comparative charts have
been developed to provide a platform for comparing the
RAM-related criteria of different FCs. The opinions of
experts have been aggregated using fuzzy methodology to
convert the linguistic scale to a crisp value. Consequently,
the AHP methodology is used in this study to rank the
performance criteria.
The TGTO type was found to have the lowest performance owing to low reliability and maintainability. Therefore, infrastructure managers must give priority to TGTO
converters for maximum improvement of availability. In
addition, the Q48 type is found to have the lowest availability because of its low maintainability, and the YRLA
also has low reliability.
References
[1] Y.A. Mahmood, A. Ahmadi, R. Karim, U. Kumar, A.K.
Verma, and N. Fransson, Comparison of frequency converter
outages: A case study on the Swedish TPS system, Proc. of
Intern. Conf. of World Academy of Science, Engineering and
Technology, Issue No. 71, Paris 2012, 2298.
[2] J. Setreus, P. Hilber, S. Arnborg, and N. Taylor, Identifying
critical components for transmission system reliability, IEEE
Transactions on Power Systems, 27(4), 2012, 2106–2115.
[3] S.K. Chen, T.K. Ho, and B.H. Mao, Reliability evaluations
of railway power supplies by fault-tree analysis, IET Electric
Power Applications, 1(2), 2007, 161–172.
[4] Y.A. Mahmood, A. Ahmadi, and A.K. Verma, Identification
of frequency converter models for availability improvement,
International Journal of Electrical Engineering, 20, 2013,
159–169.
[5] T.E. Ekstrom, Reliability/availability guarantees of gas turbine
and combined cycle generating units, IEEE Transactions on
Industry Applications, 31(4), 1995, 691–707.
10
Ajit K. Verma is a professor in
engineering (technical safety),
Stord/Haugesund University College, Haugesund, Norway, and
has been a professor 2001 with
the Department of Electrical Engineering at IIT Bombay. He
was the director of the International Institute of Information
Technology Pune, from August
2009 to September 2010.
He
has supervised/co-supervised 35
Ph.D.s and 95 masters’ theses in the area of reliability, reliable computing, power systems reliability and probabilistic
safety/risk assessment.
Biographies
Yasser A. Mahmood is a lecturer
in University of Mosul; Mosul,
Iraq. He completed his bachelor and master degrees in Electrical Engineering Department in
University of Mosul in 1997 and
2005, respectively. Now, he is a
Ph.D. student in Operating and
Maintenance Department in Lule˚
a
University of Technology, Lule˚
a,
Sweden, and working on the RAM
analysis of traction power supply
system.
Alireza Ahmadi is an assistant professor at the Division of
Operation and Maintenance
Engineering, Lule˚
a University of
Technology (LTU), Sweden. He
has received his Ph.D. degree in
operation and maintenance engineering in 2010. Alireza has more
than 10 years of experience in civil
aviation maintenance as a licensed
engineer and production planning
manager. His research topic is
related to reliability, safety and maintenance optimization.
11
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