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 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, feature sets 11 kinds monitoring data optimized and selected based on MIC for system. By eliminating redundant variables insensitive variables, variable great influence selected. Second, by upgrading selection method control parameter σ from linear nonlinear, GWO-SVM that can take into account both global local search capabilities is Furthermore, optimal set which has as input proposed algorithm, then combining constructed The specific flow step given. Finally, compared other simulation experiments show a higher accuracy recall in