Gas insulated switchgear(GIS)may encounter various types of insulation faults,of which the accurate identifi-cation is of great significance to power safety.This work studied a GIS partial discharge(PD)classification and identifica-tion method based on the rime optimization algorithm(RIME),which optimizes variational mode decomposition(VMD)and least squares support vector machine(LSSVM).First the RIME was introduced with minimum envelope entropy as the objective function for K and in VMDαOptimize two parameters.Then IMFs was selected and features were extracted using kurtosis,margin,and waveform.Finally the extracted feature vectors were input into RIME-LSSVM for identifica-tion and diagnosis.The proposed method was indicated,by testing treatment of four types of PD ultra-high frequency sig-nals,to have better diagnostic performance with accuracy improvement up to 16%and achieve effective identification of different fault types compared to the selected conventional algorithms,and thereby to be potentially significant for fault i-dentification of high-voltage power equipment such as GIS.
维普
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