Fault Diagnosis Method of Transformer Based on IGCSSA-SVM
In response to the problem of low accuracy in transformer fault diagnosis,based on dissolved gas analysis technology in trans-former oil,this paper proposes an improved sparrow search algorithm(IGCSSA)optimized support vector machine(SVM)transformer fault diagnosis model.Firstly,the traditional Sparrow algorithm is improved using elite reverse and Gaussian Cauchy mutation strategies,and then the SVM algorithm is optimized using the Improved Sparrow Search Algorithm(IGCSSA).Finally,compare the diagnostic results of Sparrow Algorithm Support Vector Machine(SSA-SVM),Grey Wolf Algorithm Support Vector Machine(GWO-SVM),and Particle Swarm Optimization Support Vector Machine(PSO-SVM).The test results show that the IGCSSA-SVM model has an accuracy of 94%,which is 9%,19%,and 18%higher than SSA-SVM,GWO-SVM,and PSO-SVM models,respectively,effectively improving diagnos-tic accuracy.
NETL
NSTL
万方数据
维普
