Research on Fault Diagnosis Method of Power Transformer Based on KPCA and IHHO-LSSVM

In order to improve the accuracy of power transformer fault identification, a power transformer fault diagnosis method based on kernel principal component analysis(KPCA) and improved Harris hawk algorithm(IHHO) optimized least square support vector machine(LSSVM) is proposed. First, use nuclear principal component analysis to preprocess the original fault data of the transformer to remove redundant data. Secondly, combine the Sigmoid deformation function and the point symmetry strategy to improve the traditional Harris hawk optimization(HHO), and combine with HHO and genetic algorithm(GA). The performance comparison proves that the accuracy of the solution and the speed of network convergence have been improved. Finally, IHHO is used to optimize the related hyperparameters of LSSVM to obtain a transformer fault diagnosis model combining KPCA and IHHO-LSSVM. The results show that the diagnosis accuracy of the proposed model is 95.6%, which is 8.9% and 16.7% higher than other fault diagnosis models, respectively, which proves that the proposed method can effectively improve the performance of transformer fault diagnosis.