Abnormal Diagnosis Method of Self-Powered Power Supply System Based on Improved GWO-SVM

In order to solve the problem of low abnormal diagnosis rate of self-powered power supply system, an improved grey wolf optimization-support vector machine (GWO-SVM) algorithm combined with maximal information coefficient (MIC) are proposed. First, the feature sets of 11 kinds of monitoring data are optimized and selected based on MIC for self-powered power supply system. By eliminating redundant variables and insensitive variables, feature variable sets with great influence on abnormal diagnosis are selected. Second, by upgrading the selection method of control parameter σ from linear to nonlinear, an improved GWO-SVM algorithm that can take into account both global and local search capabilities is proposed. Furthermore, the optimal feature set which has great influence on abnormal diagnosis is selected as the input of the proposed algorithm, and then the abnormal diagnosis method combining the improved GWO-SVM with MIC is constructed for self-powered power supply system. The specific algorithm flow and step are given. Finally, compared with other algorithm, the simulation experiments show that the GWO-SVM method has a higher accuracy and a higher recall rate for the abnormal diagnosis in the self-powered power supply system.