The study of fault diagnosis model of DGA for oil-immersed transformer based on SVM active learning and K-L feature extracting

Abstract

A model based on support vector machine (SVM) active learning and Karhunen-Loeve(K-L)feature extracting is proposed for oil-immersed transformer fault diagnosis, and a SVM active learning algorithm with the Euclidian distance based on Mercer function is introduced to select the training sample data. The K-L transform is used to extract the characteristics of the sample data set, and the sample data set that has reduced six dimensions to three dimensions is showed in the three-dimensional figure. The SVM active learning algorithm is used to select and classify the fault types. The result shows that the precision is better than the traditional one, and the reliability and effectiveness using above method is satisfied in fault diagnosis.