Transformer Fault Diagnosis Based on Hierarchical Multi-class SVM

Abstract

According to the relationship between dissolved gases in transformer oil and transformer fault, a transformer fault diagnosis model and related solving steps are proposed derived from multi-class SVM theory. Based on the concept of feature extraction in pattern recognition, a hierarchical structure is employed to extract the input features closely related to the model of classification, and it has effectively suppressed the interference of redundant information. By comparison of the diagnosis results, the best extracting mode is selected. Besides, the adaptive parameter optimization algorithm has both increased the ¿exibility of parameter selection for SVM and enhanced the convergence speed. The results of the final test reveal that the hierarchical multi-class SVM is of high accuracy and excellent generalization.