Predicting Transformer Lifetime Using Survival Analysis and

SAS Analytics Day
Predicting Transformer Lifetime Using Survival Analysis and Modeling Risk Associated with Overloaded Transformers
Balamurugan Mohan
4/28/2015
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RIGHT CENSORED DATA
DATA COLLECTION
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OBJECTIVES
 Using survival analysis to predict the life time of transformers
 PROC LIFE TEST models for failure time data are built so as to see the lifetime of transformers based on age and overloaded strata
 Find the important factors that contribute to the failure of the transformer using Cox’s Proportional Hazards model
 Build transformer risk based models to identify the transformers that get overloaded in advance
DATA ANALYSIS
Age Based Stratum
DATA ANALYSIS
Overload Based Stratum
COX’S PROPORTIONAL HAZARD MODEL
PHREG Model Results
LOAD BASED ANALYSIS
Model Comparison
CATEGORY BASED ANALYSIS
Model Comparison
CONCLUSION
 The predominant failure age of transformers are between 1,500 to 1,625 days
 Overloaded count, Average temperature, Average kVA rating, and the Normal count of the transformers are the important factors contributing to hazard rate
 The commercial transformers fail faster when compared to the residential transformers
 Models built to predict the risk factor associated with the transformers suggest us that Age, Average kVA rating and Normal condition are the important factors contributing to the overloading of the transformers
FUTURE SCOPE
 An Interactive daily dashboard showing high risky transformers can be built so that transformers they can be maintained then and there.  There can be other reasons for risk such as failure of insulation material, manufacturing errors, oil contamination, line surge, Improper maintenance and lightning etc.
CONTACT INFORMATION
 Your comments and questions are valued and encouraged. Contact me at:
Balamurugan Mohan
Oklahoma State University
916‐768‐4559
[email protected]
 I thank Dr. Goutam Chakraborty, Professor, Department of Marketing and founder of SAS and OSU Data Mining Certificate program ‐ Oklahoma State University for his support throughout the research.
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