Feature Selection of XLPE Cable Condition Diagnosis Based on PSO-SVM

In order to improve the accuracy of crosslinked polyethylene (XLPE) cable status diagnosis and eliminate redundant features, this paper proposes a feature selection method for XLPE cable status diagnosis based on particle swarm optimization algorithm to optimize support vector machine. Firstly, this paper constructs 31 binary coded feature combinations based on five commonly used feature parameters for XLPE cable detection. Secondly, the optimal feature subset is selected based on the average accuracy of 20 cross-validation of PSO-SVM under 31 different feature combinations. Finally, train the diagnostic model with the optimal feature subset and its corresponding parameters, and verify the diagnostic performance of the selected features with test set samples and XLPE cable field data. The experimental results show that the PSO-SVM feature selection method can effectively find the optimal feature subset. The number of features selected in this paper is 2, and the classification accuracy of the test set is 98%. Compared with the preferred features of traditional feature selection methods, the features selected in this paper have good generalization ability.