Application of improved particle swarm optimization BP neural network in transformer fault diagnosis

A power transformer fault diagnosis method based on Improved Particle Swarm Optimization and BP neural network is proposed. The particle swarm algorithm that used to optimize the parameters of the BP neural network is prone to “premature”. By optimizing the inertia weight, in the process of increasing the number of iterations, the inertia weight can be gradually reduced, and the algorithm can avoid the partial optimization. By introducing momentum terms, the weights adjusting method of BP neural network is optimized. The training speed of BP neural network is speeded up and the training accuracy is improved. Simulation results show that the improved PSO algorithm can optimize the BP neural network effectively in transformer fault diagnosis, and improve the efficiency of diagnosis.