Detection of Winding Faults Using Image Features and Binary Tree Support Vector Machine for Autotransformer

Abstract

Autotransformer (AT) is the most core power supply equipment, and overvoltage and short circuit (SC) fault may lead to winding deformation, which will have a negative impact on its insulation and even affect the operation of a train. The frequency response analysis (FRA) is widely used for detecting winding faults in a transformer. However, the direct measure of FRA for each split winding fails because the split windings are adopted to satisfy the impedance requirement of a high-speed railway, where the windings are connected inside the tank. A novel fault interpretation method based on image features and binary tree support vector machine (SVM) is proposed, which can get the condition of three windings in one measurement. Winding faults caused by different windings are simulated, including SC defect, axial deformation, and series capacitance variation, and the FRA curves are measured under various faults. Then, the features of the gray-level gradient co-occurrence matrix and the gray-level difference statistics are got from the polar plot of FRA. Finally, the image features are used as the inputs to the binary tree SVM for fault type and faulty winding classification. The results show that the proposed method has high accuracy for identifying fault type and faulty winding in AT.