Power transformer fault diagnosis using support vector machines and artificial neural networks with clonal selection algorithms optimization

Authors:
Ming-Yuan Cho;Tsair-Fwu Lee;Shih-Wei Gau;Ching-Nan Shih
Affiliations:
Department of Electrical Engineering, National Kaohsiung University of Applied Science, Kaohsiung, Taiwan, ROC;Department of Electrical Engineering, National Kaohsiung University of Applied Science, Kaohsiung, Taiwan, ROC;Department of Electrical Engineering, National Kaohsiung University of Applied Science, Kaohsiung, Taiwan, ROC;Department of Electrical Engineering, National Kaohsiung University of Applied Science, Kaohsiung, Taiwan, ROC
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

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Abstract

This paper presents an innovative method based on Artificial Neural Network (ANN) and multi-layer Support Vector Machine (SVM) for the purpose of fault diagnosis of power transformers. A clonal selection algorithm (CSA) based encoding technique is applied to improve the accuracy of classification, which demonstrated in the literature for the first time. With features and RBF kernel parameters selection to predict incipient fault of power transformer improve the accuracy of classification systems and the generalization performance. The proposed approach is distinguished by removing redundant input features that may be confusing the classifier and optimizing the selection of kernel parameters. Simulation results of practice data demonstrate the effectiveness and high efficiency of the proposed approach, which makes operation faster and also increases the accuracy of the classification.