Power transformer fault diagnosis based on improved PSO - BP hybrid algorithm
WEI Xing,SHU Nai-qiu,ZHANG Lin,CUI Peng-cheng (Department of Electrical Engineering,Wuhan University,Wuhan 430072,China)
The hybrid algorithm combining improved PSO(Particle Swarm Optimization) algorithm with BP(error Back Propagation) algorithm is used to train the artificial neural network. To balance and reconcile the global and local searching capability,the inertia weight of improved PSO is reduced linearly from maximum to minimum and the cencept of "selection" is introduced into the PSO to find the global optimal point more quickly. Defects of conventional BP algorithm,i.e. the slow convergence of weight and threshold learning,premature result,and the slow training speed of GA,are settled by it. Its application in power transformer fault diagnosis is simulated. Results show that it meets the requirements of power transformer fault diagnosis for both convergence speed and calculation accuracy.
【CateGory Index】： TM41