An Intelligent Genetic Fuzzy Classifier for Transformer Faults
Amit Kukker, Rajneesh Sharma, Hasmat Malik
Abstract
Classification of faults in transformers with high accuracy is fundamental to ensuring good power quality with least interruptions. Our current work develops an intelligent genetic algorithm (GA)-tuned fuzzy classifier for transformer fault identification. The proposed classifier is able to segregate all fault types using dissolved gas analysis (DGA) samples from real power transformers of HPSEB (India) and other sources. DGA samples have been pre-processed using the J48 algorithm. We propose to replace the conventional action selection procedure of reinforcement learning by a GA-based optimizer. The classifier is able to garner very high classification accuracy which is higher than the one obtained with benchmark fuzzy Q learning (FQL) and other conventional classifiers. With our approach, the average fault classification rate achieved is 91.85% (FQL) and 97.51% genetic algorithm fuzzy Q-learning (GAFQL) though with a slightly higher computational complexity over the FQL. Our proposed classifier could serve as an important tool in ensuring the healthy operation of power transformers.