Transformer Fault Diagnosis Using Hybrid Feature Selection and Improved Black-Winged Kite Optimized SVM

In order to solve the problems of difficulty in extracting effective features from dissolved gases in transformer oil and limited recognition accuracy of the fault diagnosis model, a feature selection and improved black-winged kite algorithm (IBKA) optimized support vector machine (SVM) transformer fault diagnosis method based on dissolved gas analysis (DGA) in oil is proposed. Firstly, a hybrid feature selection method is used to perform quantitative analysis on the constructed 20-dimensional fault candidate feature set, thereby achieving the selection of feature variables. Then, the Tent chaotic mapping, the Gompertz growth model, and the Morlet wavelet variation strategy are introduced to improve the Black-Winged Kite Algorithm (BKA) to enhance its optimization searching performance; then, the IBKA is used to optimize the hyperparameters such as kernel function and penalty factor of SVM to improve the accuracy of model diagnosis results. Finally, case analysis based on 410 sets of IEC TC10 transformer fault data shows that the fault diagnosis accuracy of the proposed method reaches 98.37%, which verifies the effectiveness of the proposed method for classifying faults according to the IEC TC10 method.