Transformer Fault Dignosis Based on Feature Selection and Parameter Optimization

Failure of transformer is very complex, dissolved Gas in Oil Analysis (DGA) is presently the easier and simpler way for fault diagnosis of oil-immersed transformers. The correct selection of features of dissolved gas data can improve efficiency of transformer fault diagnosis. SVM is more effective than traditional methematic model to discribe the type of fault of transformer. As for the problem of difficulty of determining parameters in SVM applications, genetic algorithm (GA) was used to select SVM parameters. The test results show that this GA-SVM model is effective to detect failure of transformer. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of University of Electronic Science and Technology of China (UESTC).