A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM
[摘要] In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.
[发布日期] [发布机构] Electric Power Scientific Research Institute of Guangxi Power Grid, Guangxi Power Grid Corporation, Nanning, China^1
[效力级别] 无线电电子学 [学科分类]
[关键词] Cross validation;Dissolved gas in oil analysis;Fault diagnosis model;Genetic algorithm support vector machines;Multi-classification;Parameter optimization;Test method;Transformer fault diagnosis [时效性]
