A relevance vector machine and bare-bones particle swarm optimization hybrid algorithm for PD pattern recognition of XLPE cable
In the paper, a relevance vector machine and bare-bones particle swarm optimization hybrid algorithm (BBPSO-RVM) is first presented and used to recognize the partial discharge patterns in XLPE power cables, and the bare-bones particle swarm optimization (BBPSO) is proposed to select the parameters of relevance vector machine (RVM). In the study, 200 cases are collected to testify the effectiveness of the relevance vector machine and bare-bones particle swarm optimization hybrid algorithm compared with other classifiers. Traditional relevance vector machine classifier, support vector machine classifier and RBF neural network are used to compare with the relevance vector machine and bare-bones particle swarm optimization hybrid algorithm. Finally, the outputs of the relevance vector machine and bare-bones particle swarm optimization hybrid algorithm, relevance vector machine classifier, support vector machine classifier (SVM) and RBF neural network (RBFNN) are given, and the comparison of PD pattern recognition accuracies of XLPE cable among BBPSO-RVM, RVM, SVM and RBFNN is given. The experimental results show that recognition accuracy of BBPSO-RVM is higher than that of RVM, SVM and RBFNN. In the study, 200 cases are collected to testify the effectiveness of the relevance vector machine and bare-bones particle swarm optimization hybrid algorithm compared with other classifiers. Traditional relevance vector machine classifier, support vector machine classifier and RBF neural network are used to compare with the relevance vector machine and bare-bones particle swarm optimization hybrid algorithm. The outputs of the relevance vector machine and bare-bones particle swarm optimization hybrid algorithm, relevance vector machine classifier, support vector machine classifier and RBF neural network are given. It can be seen from the comparison of PD pattern recognition accuracies of XLPE cable among BBPSO-RVM, RVM, SVM and RBFNN that recognition accuracy of BBPSO-RVM is higher than that of RVM, SVM and RBFNN.
