Transformer diagnostics under dissolved gas analysis using Support Vector Machine

Abstract

Power transformer is vital equipment in any electrical power system. So any fault in the power transformer may lead to the interruption of power supply and accordingly the financial losses will also be great. So it is important to detect the incipient faults of transformer as early as possible. Among the existing methods for identifying the incipient faults, dissolved gas analysis (DGA) is the most popular and successful method. Any kind of fault inside transformer gives rise to overheating and will produce characteristics amount of gases in transformer oil. In this paper classical methods of DGA such as Key Gas Method, Rogers Ratio Method and Duval Triangle Method are reviewed first and the need to integrate with the artificial intelligence (AI) methods for improving the performance of diagnosis is justified. Reported work presents a new and efficient artificial intelligence technique that is support vector machine (SVM) for transformer fault diagnosis using dissolved gas analysis. The proposed method i.e. Support Vector Machine is a classification tool based on statistical learning theory. Here 3 types of multiclass SVM method that is One - against-One, One-against-All and binary decision tree have been used for the fault diagnosis. Each SVM method has been trained and tested with many practical fault data of power transformers.