Fault Diagnosis Method for Transformer Based on KPCA and SSA-SVM

Abstract

For the sake of integrate the ratio of dissolved gas in the oil and the advantages of artificial intelligence technology to enhance the accuracy rate of transformer fault diagnosis, the fault diagnosis method for transformer based on kernel principal component analysis (KPCA)and sparrow search algorithm (SSA)-support vector machine (SVM) was proposed. Firstly, based on the oil dissolved gas analysis (DGA), 24 fault features of the power transformer were extracted, Secondly, KPCA was used for dimensionality reduction to obtain a feature space with lower latitude. The fault diagnosis model of transformers based on SVM was designed with the 8 selected features as inputs, and the parameters in the model were simultaneously optimized by SSA. Then, SSA-SVM was adopted to diagnose the typical working conditions. To prove the superiority of the SSA-SVM diagnostic model combined with KPCA feature space, the comparative experiment of SSA-SVM classifier results in the origin feature space, the KPCA feature space was carried out, the comparison for accuracy of various methods in the KPCA feature space was proceed as assist.KeywordsDissolved gas analysisPower transformersFault diagnosisKernel principal component analysisSparrow search algorithm