Fault diagnosis of power transformer based on multi-layer SVM classifier

Abstract

Support vector machine (SVM) is a novel machine learning method based on statistical learning theory (SLT). SVM is powerful for the problem with small sampling, nonlinear and high dimension. A multi-layer SVM classifier is applied to fault diagnosis of power transformer for the first time in this paper. Content of five diagnostic gases dissolved in oil obtained by dissolved gas analysis (DGA) is preprocessed through a special data processing, and six features are extracted for SVMs. Then the multi-layer SVM classifier is trained with the training samples which are extracted by the above data processing. Finally, the four fault types of transformer are identified by the trained classifier. The test results show that the classifier has an excellent performance on training speed and reliability. © 2005 Elsevier B.V. All rights reserved.