A transformer fault diagnosis method based on hybrid improved grey wolf optimization and least squares-support vector machine

Abstract

In recent years, support vector machine has been widely used in the field of transformer fault diagnosis, but its fault diagnosis performance is still to be improved. A power transformer fault diagnosis model based on hybrid improved grey wolf optimization optimized least squares-support vector machine will be proposed as an attempt to improve the efficiency and accuracy of its fault diagnosis performance. First, the hybrid improvement method will be introduced with a view to solving the three problems that grey wolf optimization is troubled with, that is, premature convergence, slow convergence, and low population diversity. Then, six benchmark functions and Wilcoxon rank sum test are used to test hybrid improved grey wolf optimization and the other optimization algorithms, and the results show that hybrid improved grey wolf optimization has better optimization performance and significance. Finally, dissolved gas analysis data will be used for example simulation which will be further examined from three aspects in order to testify the fault diagnosis performance of the proposed method. The results show that the validity of the proposed method is much higher than that of the other models. Therefore, it can be concluded that the proposed method can greatly improve the performance of transformer fault diagnosis, and has great practical significance in engineering.