Fault diagnosis of cascaded inverter based on PSO-BP neural networks

摘要：

　　Aiming at the power component open circuit faults of the cascaded inverter,the fault model is set up,and the PSO-BP neural network is used to diagnose the faults.At the same time,in order to avoid the premature convergence in the basic PSO algorithm,some mutation operations are conducted upon the particles.The wavelet decomposition is used to extract the fault characteristics for training and testing,and then the improved particle swarm algorithm is used to optimize the weights and the threshold of the BP neural network.The method can improve the convergence speed of the traditional BP algorithm and avoid trapping in local minimum easily.The simulation results show that this method has higher diagnostic accuracy and faster convergence speed.It is effective for the fault diagnosis of the cascaded inverter.

关键词： fault diagnosis PSO algorithm BP Neural Networks wavelet analysis cascaded inverter

作者: WANG Xin SUN He-nan WANG Dan-lu

作者单位: School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo Henan 454000,Ch College of Information Science and Engineering,Northeastern University,Shenyang Liaoning 110819,Chin

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 3263-3267

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）