Research on Fault Diagnosis Method of Coal Mine Transformer Based on ISSA-SVM
In order to effectively improve the fault diagnosis accuracy of coal mine transformer,a new method of coal mine transformer fault diagnosis based on ISSA-SVM is proposed by analysing the connection between dissolved gases in transformer oil and fault types.Kernel Principal Component Analysis(KPCA)is used to extract features from coal mine transformer data;Logistic chaotic mapping and Gaussian Cosi-variance operator are used to improve the traditional Sparrow Algorithm(SSA),and the experimental results of the benchmarking test function show that the ISSA optimisation seeking ability and convergence speed have been greatly improved.The parameters of SVM are optimised by ISSA to establish the coal mine transformer fault diagnosis method model,and the experimental results show that the diagnostic accuracies of ISSA-SVM,PSO-SVM,and SSA-SVM are 94.91％,80.84,and 86.33％,respectively,and that ISSA-SVM is effective in improving the diagnostic accuracies of coal mine transformer.
NETL
NSTL
万方数据
维普
