Diagnosis Method of Transformer Winding Fault Based on Bald Eagle Search Optimizing Support Vector Machines

At present, the most widely used method of winding fault detection is frequency response analysis (FRA). To surmount the defects of slow convergence and low classification accuracy caused by inappropriate parameter selection of support vector machines (SVM), this paper proposes a transformer winding fault classification method based on bald eagle search (BES) algorithm to optimize the kernel parameter g and the penalty coefficient C in the SVM model, denoted as BES-SVM. The analysis of transformer winding faults shows that the BES- SVM model can diagnose faults more accurately. Compared with the traditional SVM and PSO-SVM methods, this method has higher diagnostic accuracy.