组合核支持向量机在放电模式识别中的优化策略

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Abstract Conventional single kernel SVM has natural defects on mapping multiple partial discharge(PD) feature spaces and classifying multiple PD types. Most popular SVM classifiers adopt RBF with different parameters as kernel functions that limited the adjustment space; moreover the universality to process multiple feature spaces is missed. Aiming at these problems, a grouped-feature based combined-kernel multiclass support vector machine(CKM-SVM) is proposed. Multiple PD feature spaces are constructed and mapped to different SVM kernel functions; then each kernel function is optimized via bare-bone particle swarm optimization(BBPSO), and then the weight coefficients for CKM-SVM model are calculated. Tests show that CKM-SVM performs good feature spatial fusion; additionally the recognition accuracy precedes BPNN and SVM.

Key words： PD combined-kernel SVM BBPSO pattern recognition

Received: 29 September 2014 Published: 14 April 2015

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	Articles by authors
	Wang Yu
	Yuan Jinsha
	Shang Haikun
	Jin Song

Cite this article:

Wang Yu,Yuan Jinsha,Shang Haikun等. Optimization Strategy Research on Combined-Kernel Support Vector Machine for Partial Discharge Pattern Recognition[J]. Transactions of China Electrotechnical Society, 2015, 30(2): 229-236.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2015/V30/I2/229

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