First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06)
Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection
Beijing, China
August 30-September 01
ISBN: 0-7695-2616-0
Ming-Yuan Cho, National Kaohsiung University of Applied Science, Taiwan, ROC
Tsair-Fwu Lee, National Kaohsiung University of Applied Science, Taiwan, ROC
Shih-Wei Kau, National Kaohsiung University of Applied Science, Taiwan, ROC
Chao-Ji Chou, National Kaohsiung First University of Science and Technology, Taiwan, ROC
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.
Citation:
Ming-Yuan Cho, Tsair-Fwu Lee, Shih-Wei Kau, Chin-Shiuh Shieh, Chao-Ji Chou, "Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection," icicic, vol. 1, pp.26-30, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06), 2006
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