Modified PSO-based Artificial Neural Network for Power Electronic Devices Fault Diagnosis Modeling
Particle swarm optimization (PSO) algorithm has been proven to be effective for neural network (NN) training. But in some cases, standard PSO converges prematurely without finding global optimum. In this paper a modified PSO (MPSO) is introduced to address the issue of premature. A novel search mechanism is proposed and the diversity of the population is controlled then. That is, the exploration and exploitation of search space are increased, resulting in avoiding premature convergence. 294 fault states of 12-pulse waveform controlled rectifier circuit are studied. A three-layer NN is employed to construct a fault mapping and MPSO is used as training algorithm. Simulation and experiment study demonstrate that the proposed technique is effective with high fault identification rate. It is suitable for the fault diagnosis of complex power electronic devices.
particle swarm optimization artificial neural network fault detection
Xiujuan Liu Xiumin Liu
Faculties of Architecture and Art Design Huzhou Vocational & Technical College Huzhou, Zhejiang Prov Zhejiang Provincial Office of Housing and Urban-Rural Development Cadre School Hangzhou, Zhejiang Pr
国际会议
成都
英文
1347-1351
2010-12-17（万方平台首次上网日期，不代表论文的发表时间）