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Fault diagnosis for generator unit based on RBF neural network optimized by GA-PSO

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4 Author(s)
Yu-liang Qian ; School of Electronic and Information, Tongji University, Shanghai, China ; Hao Zhang ; Dao-gang Peng ; Cong-hua Huang

PSO (Particle Swarm Optimization)-RBF is widely used in intelligent fault diagnosis for generator unit. Since PSO has slow convergence rate, low accuracy, and early-maturing problem which effect training speed and diagnosis accuracy of PSO-RBF, the operations of crossover and variation of genetic algorithm (GA) are introduced into PSO such that the performance of PSO can be improved. GA-PSO is employed to optimize the RBF neural network with concrete steps, then GA-PSO-RBF is applied in fault diagnosis for generator unit. Simulation results show that GA-PSO-RBF is superior to PSO-RBF in training speed, convergence accuracy, and diagnosis accuracy, thus, it is a new efficient diagnosis approach.

Published in:

Natural Computation (ICNC), 2012 Eighth International Conference on

Date of Conference:

29-31 May 2012