1. No category

INTERNATIONAL JOURNAL OF CONTROL, AUTOMATION AND SYSTEMS
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ISSN 2165-8277 (Print) ISSN 2165-8285 (Online)
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APRIL 2015
Artificial Neural Networks for Power Transformers Fault Diagnosis Based on
IEC Code Using Dissolved Gas Analysis
Sherif S. M. Ghoneim1, 2 and Ibrahim B. Taha1, 3
The most five criteria that are commonly used for
dissolved gas analysis are the International Electrotechnical Commission standard (IEC) Code, the Central
Electric Generating Board (CEGB) Code based on Rogers
four ratios, Rogers’ method, Dornenburg method and
Duval triangle according to the Institute of Electrical and
Electronics Engineers standard (IEEE-C57) [3].
The above criteria do not involve any mathematical
formulation and their interpretations are based on
heuristic methods that may vary based on experience of
the analyst, results in unreliable analysis [4]. To
overcome the drawbacks come from these criteria,
various
computational
models
using
Artificial
Intelligence (AI) have been used to analysis the incipient
fault in power transformer. Application of AI in
transformer incipient fault diagnosis requires real DGA
data. During the period of 1987-2012, there were over
400 research published on IEEE (26 EPSs, 72 ANNs, 58
FL and ANN-FL, 20 ANN-EPS; 248 DGA and related
ones) [5]. Several AI methods have been developed for
more accurate diagnosis. These methods are mostly
suitable for transformers with a single fault or a dominant
fault. These AI methods are: Artificial Neural Network
(ANN) [6,7], Fuzzy Logic [8, 9], Neuro–Fuzzy [10, 11],
Genetic [12, 13], Hidden Markov Model (HMM) [14],
Support Vector Machine (SVM) [15, 16], and Graphical
Techniques [17, 18]. They were developed as a novel
technique to interpret the faults in power transformers.
In this paper, back propagation ANN model is
constructed based on DGA of the IEC Standard rules
method. A comparison between the results of the ANN
and that obtained from the literatures is presented. The
results refer to the reliability of the proposed ANN model
as a diagnostic tool for incipient power transformer fault.
Abstract—Transformer is the main important equipment in
electrical power system. Early stage detection of the
transformer faults has great economic significance because
it considered expensive equipment and it helps to maintain
the continuous operation of the electrical power system.
Transformer oil is used for two main purposes, one for
insulating liquid and the other for cooling. Some physicalchemical tests are carried out to determine the physical and
chemical properties of the oil. Dissolved Gas Analysis (DGA)
is now considered a common practice method for detection
of the transformer incipient fault. This paper focuses on the
employment of the Artificial Neural Network techniques
(ANN) to diagnose dissolved gas in transformers, in order to
determine the fault causes based on the IEC standard
method. The ANN on IEC Code results meets the similar
results of the other techniques that use to diagnose the
transformer fault. Therefore, this method is very reliable to
use as a diagnostic tool for transformer fault detection.
Keywords—Transformer faults, Dissolved gas analysis,
Neural Networks.
I. INTRODUCTION
Power transformer is considered as one of the most vital,
important and expensive components in electric power
systems. Any fault in power transformer may result in
power outages and black-outs of the electrical power
system. Therefore, the early detection of the power
transformer incipient faults lead to an improvement in
power system reliability and operation. Moreover, the
replacement of a power transformer is very costly and
time consuming; hence it is very important to diagnose
incipient faults as soon as possible to prevent an increase
of the transformer faults
Dissolved Gas Analysis (DGA) in the transformer oil is a
wide spread method that is used to identify the incipient
faults in oil-filled power transformers. There are different
stresses affect on the insulating transformer oil, which are
electrical and thermal stresses due to arcing, corona
discharges, sparking, or overheating fault. As a result of
these stresses, insulating materials may be damaged and
several gases are released. The main dissolved gases in
the transformer oil are: hydrogen (H2), methane (CH4),
ethane (C2H6), ethylene (C2H4), acetylene (C2H2),
carbon monoxide (CO) and carbon dioxide (CO2). The
detection methods based on dissolved gas analysis are
used to diagnose the incipient fault in power transformer
before deteriorating to a severe state [1-2].
II. DISSOLVED GAS ANALYSIS (DGA): IEC
STANDARD CODE
The IEC three-ratio method is widely used as a guideline
and a standard in diagnosis stage as it is being one of the
effective and convenient guidelines and available
standards [4]. Table 1 shows the relations between the
three-ratios and the method codes while Table 2 tabulates
the IEC standard fault types in power transformer. It
consists of three key-gas ratios corresponding to the
suggested fault diagnosis.
1
College of Engineering Taif University, Saudi Arabia Kingdom
Faculty of Industrial Education, Suez University, Suez, Egypt,
[email protected], 3Faculty of Engineering,
Tanta University, Tanta, Egypt. [email protected]
2
18
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ISSN 2165-8277 (Print) ISSN 2165-8285 (Online)
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working with the back-propagation learning algorithm
[21, 22].
Table 1 IEC Code [1]
Gas Ratio
R1=C2H2/C2H4
R2=CH4/H2
R3=C2H4/C2H6
Value
Code
R1<0.1
0
0.1≤R1≤3
1
R1>3
2
R2<0.1
1
0.1≤R2≤1
0
R2>1
2
R3<1
0
1≤R3≤3
1
x1
y1
b1
x2
y2
b2
Inputs
Outputs
xn
yn
bn
R3>3
2
Table 2 Fault diagnosis using IEC Code
Input Layer
Hidden Layers
Output Layer
Fig. 1: MLP neural network training
The output from the sigmoid function lies between 0 and
1. The mean square error is proposed to a level of 0.0001,
where a satisfactory agreement is found between the
training set results and the network result. In this study 74
samples of DGA were provided by the electrical utility
and 125 samples were taken from DGA results published
in the literatures [23, 30]. All 199 samples are used for
validating the neural network model.
Code
No.
APRIL 2015
Fault Type
R1
R2
R3
1
No fault
0
0
0
3
Partial discharge with low energy
density
0
1
0
4
Partial discharge with high energy
density
1
1
0
5
Arcing discharge with low energy
1or 2
0
1
6
Arcing discharge with high energy
1
0
2
7
Thermal fault with temperature
less than 150 oC
0
0
1
This diagnosis criterion uses basically Rogers input
vector as follows:
8
Thermal fault with temperature
between 150 to 300 oC
0
2
0
[𝐼𝑛𝑝𝑢𝑡] = [𝑅1 , 𝑅2 , 𝑅3 ]𝑇
9
Thermal fault with temperature
between 300 to 700 oC
0
2
1
The output vector is build up with ten elements according
to Table 2. Ten neurons are utilized in the output layer.
10
Thermal fault with temperature
greater than 700 oC
0
2
2
IV. VALIDATION OF THE PROPOSED SMART
DIAGNOSTIC DECISION SYSTEM
2
Undetermined fault (fail to
determine the fault type)
The input and output patterns are required for Neural
network validity. Input patterns are considered the
dissolved gas ratios codes according to each fault state.
For each input pattern, there exists an output pattern that
describes the fault type. Both input and output patterns
constitute ANN training set.
Input and output patterns are defined as follows:
(1)
The validation of the proposed model is achieved by
comparing its results with the results in literatures. Some
samples are collected from some researches and
laboratory analysis then compare them with the results in
literatures. Table 3 illustrates the agreement between the
ANN results and the results in literatures. It is agreement
percentage between the results from ANN and that in
Literatures as shown in Table 3 is more than 90%. It
appears that a conflict among the results comes from the
No fault identification as well as normal operation state
with the thermal state in literatures. The results refer to
the reliability of the proposed ANN model as a diagnostic
tool for incipient fault detection.
For above codes not
obtained
III. APPLICATION OF ARTIFICIAL NEURAL
NETWORK (ANN) FOR TRANSFORMER FAULT
DETECTION
ANN model is constructed using MATLAB software for
IEC Standard code interpretation method. Figure 1 shows
the multilayer feed forward back-propagation is chosen as
the network architecture because it considers the most
popular ANN Architecture [19] and its ability for pattern
recognition [20]. The ANN architecture model consists of
four layer networks (one input layer, two hidden layer
and one output layer).
V. CONCLUSION
An artificial neural network (ANN) model is constructed
for IEC Standard code method that based on dissolved
gas analysis. To test the NN model based on IEC rules,
102 samples are used. The agreement of NN model with
A two layer perception has been utilized because of two
reasons. These are; the highly nonlinearity between the
input and due to a good performance of ANN when
19
INTERNATIONAL JOURNAL OF CONTROL, AUTOMATION AND SYSTEMS
VOL.4 NO.2
ISSN 2165-8277 (Print) ISSN 2165-8285 (Online)
http://www.researchpub.org/journal/jac/jac.html
the literatures is more than 90% of the tested cases. The
problem is experienced, the difference between the results
from the proposed model and the results from literatures
come from No fault and No fault identification states with
the thermal fault for the literatures results.
ACKNOWLEDGMENT
The authors thank Prof. Dr. Ayman A. Aly for his
valuable Discussion in construction ANN model.
REFERENCES
Table 3 Comparison between the results from ANN based on IEC
Standard interpretation method and the results from literatures and lab
analysis
H2
CH4
C2H6
C2H4
C2H2
CO
IEC state
Ref
Ref. state
269
1081
347
1725
25
360
HTH
[23]
HTH
10
10
8
1
0.01
334
NF
[23]
LTH
30
22
14
4.1
0.1
400
NF
[24]
NF
2.9
2
1.5
0.3
0.1
200
UD
[24]
NF
4
99
82
4.2
0.1
200
LTH
[24]
TH
21
34
5
47
62
390
UD
[24]
HAD
50
100
51
305
9
400
HTH
[24]
TH
120
17
32
4
23
350
UD
[24]
NF
980
73
58
12
0.01
243
PD
[24]
PD
30.8
149
47.9
146
0.1
350
HTH
[24]
TH
27
136
46.9
131
0.1
360
MTH
[24]
TH
1607
615
80
916
1294
380
HAD
[24]
HAD
14.7
3.7
10.5
2.7
0.2
1046
NF
[25]
NF
181
262
41
28
0.01
415
LTH
[25]
TH
173
334
172
812.5
37.7
404
HTH
[25]
TH
127
107
11
154
224
478
HAD
[25]
HAD
60
40
6.9
110
70
678
HAD
[25]
HAD
27
90
42
63
0.2
470
MTH
[25]
TH
980
73
58
12
0.01
243
PD
[25]
PD
86
187
136
363
0.01
26
MTH
[26]
HTH
10
24
372
24
0.01
343
LTH
[26]
MTH
30.4
117
44.2
138
0.1
380
HTH
[27]
TH
260
3
18
2
0.01
350
PD
[28]
PD
586
19
77
6
0.01
370
PD
[28]
PD
200
700
250
740
1
415
MTH
[29]
TH
33
26
6
53
0.2
678
UD
34.45
21.3
3.19
45
19.62
211
HAD
180.85
0.5
0.234
0.18
0.0001
252
PD
12
8
40
5
0.01
436
NF
16
25
19
39
0.01
383
MTH
22
40
36
6
1
422
UD
1770
3630
1070
8480
78
350
HTH
86
30
10
59.3
41
239
27.5
469
147
35
29
1014
HAD
9.9
111
70
224
HAD
5.5
25.5
85
317
LAD
12.5
265
520
211
HAD
56
5.5
92
34.5
27.5
436
PD
14
237
92
470
0.01
365
HTH
157
127
34
96
0.01
422
LTH
[29]
[29]
[29]
[29]
[29]
[29]
[29]
[29]
[30]
[30]
[30]
[30]
[30]
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APRIL 2015
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APRIL 2015
Appendix
ARC: discharge arcing
UD: undetermined fault or no fault identification
TH: thermal fault
PD: partial discharge
NF: no fault (normal operation)
HAD: high discharge energy arcing
LAD: low discharge energy arcing
HTH: high temperature thermal fault
LTH: low temperature thermal fault
MTH: medium temperature
Author' profile:
Sherif S. M. Ghoneim Received B.Sc. and M.Sc.
degrees from the Faculty of Engineering at
Shoubra, Zagazig University, Egypt, in 1994 and
2000, respectively. Starting from 1996 he was a
teaching staff at the Faculty of Industrial
Education, Suez Canal University, Egypt. Since
end of 2005 to end of 2007, he is a guest
researcher at the Institute of Energy Transport and
Storage (ETS) of the University of DuisburgEssen-Germany. In 2008, he got Ph.D Degree in Electrical power and
machines, Faculty of Engineering-Cairo University (2008). He joins
now the Taif University as an assistant professor in the Electrical
Engineering Department, Faculty of Engineering. His research focuses
in the area of Grounding systems, Dissolved gases analysis,
Breakdown in SF6 gas and artificial intelligent technique applications.
Ibrahim B. Taha Received B.Sc. degree from
the Faculty of Engineering at Tanta, Tanta
University, Egypt, in 1995. He received M.Sc.
degree from the Faculty of Engineering at
Mansoura, Mansoura University, Egypt, in
1999. Starting from 1996 he was a teaching
staff at the Faculty of Engineering, Tanta
University, Egypt. In 2007, he got Ph.D
Degree in Electrical power and machines,
Faculty of Engineering-Tanta University (2007). He joins now the Taif
University as an assistant professor in the Electrical Engineering
Department, Faculty of Engineering. His research focuses in the area
of steady state and transient stability of HVDC systems, FACTS,
Multi Level Inverters, Dissolved gases analysis, and artificial
intelligent technique applications.
21