Optimization strategy research on combined-kernel support vector machine for partial discharge pattern recognition

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. ©, 2014, Chinese Machine Press. All right reserved.