关键词: 粒子群算法, BP神经网络, 变压器故障检测

Abstract: Particle Swarm Optimization (PSO) algorithm searches the best solution by making particles moving around the search space according to the global best particle. But when one particle is selected as the global best particle continuously, the other particles will converge at the global best particle repeatedly, which makes the particle swarm fall into local optimization. The authors presented Mutational Dynamic Particle Swarm Optimization (MDPSO) algorithm. A part of particles' inertia weight would mutate when one particle was selected as the global best particle continuously, which could make the part of particles jumping out of the local optimization and keeping searching in the whole solution space. Otherwise, the authors combined MDPSO and BP neural network and applied it to the diagnosis of power transformer. The experimental results show that the proposed approach has a better ability in optimizing BP neural network and in terms of diagnosis accuracy.

Key words: Particle Swarm Optimization (PSO), BP Neural Network (BPNN), diagnosis of power transformer