Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection

Authors:
Ming-Yuan Cho;Tsair-Fwu Lee;Shih-Wei Kau;Chin-Shiuh Shieh;Chao-Ji Chou
Affiliations:
National Kaohsiung University of Applied Science, Taiwan, ROC;National Kaohsiung University of Applied Science, Taiwan, ROC;National Kaohsiung University of Applied Science, Taiwan, ROC;National Kaohsiung University of Applied Sciece, Taiwan, ROC;National Kaohsiung First University of Science and Technology, Taiwan, ROC
Venue:
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
Year:
2006

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Cited 1

Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

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Abstract

For the purpose of fault diagnosis of power transformers, a novel approach based on Artificial Neural Network (ANN) and multi-layer Support Vector Machine (SVM) is presented in the paper. The proposed approach is distinguished by features and kernel parameters selection using clonal selection algorithms (CSA). It is capable of filtering out irrelevant input features, leading to improve prediction accuracy. As revealed in the experimental results, the proposed approach outperforms previous ones in both classification accuracy and computational efficiency.