Fault Diagnosis Method of Transformer Based on ANOVA and BO-SVM

In order to improve the diagnostic accuracy of transformer fault diagnosis model, this paper proposes a transformer fault diagnosis method based on analysis of variance (ANOVA) and bonobo optimizer-support vector machine (BO-SVM). Firstly, ANOVA is used to filter and reduce the dimensionality of the model input quantity(namely, the dissolved gas characteristics) in transformer oil. Secondly, the bonobo optimizer is used to optimize the two parameters (kernel parameter and penalty factor) that affect the diagnostic performance of support vector machine. Finally, the proposed method is used for transformer fault diagnosis. The simulation results show that, compared with IEC and Rogers methods, using ANOVA for screening and dimensionality reduction of the model input can improve model performance. Compared with support vector machine optimized by grid search algorithm, BO-SVM improves both the training speed and the diagnostic accuracy. Compared with particle swarm optimization-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM), the proposed method has faster convergence rate, higher diagnostic accuracy, and more stability. The average diagnostic accuracy of BO-SVM, PSO-SVM and GA-SVM are 91.69%, 83.29% and 81.34%,respectively, and the superiority of the proposed method is verified. © 2023 Science Press. All rights reserved.