- Volume 12 Issue 2
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A New Support Vector Machine Model Based on Improved Imperialist Competitive Algorithm for Fault Diagnosis of Oil-immersed Transformers
- Zhang, Yiyi (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University) ;
- Wei, Hua (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University) ;
- Liao, Ruijin (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
- Wang, Youyuan (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University and State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
- Yang, Lijun (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
- Yan, Chunyu (China Electric Power Research Institute)
- Received : 2016.06.20
- Accepted : 2016.09.26
- Published : 2017.03.01
Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.
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