基于小波神经网络的六相永磁同步电机高阻连接状态感知策略OACSTPCD

Six-phase permanent magnet synchronous motors have the ability of phase-deficient operation,thus precise prediction of their high resistance connection state must be made to ensure effective disconnection for faulty lines,prevent protection misoperation caused by system disturbances,and provide reliable criteria for fault-tolerant control.A mathematical model for complete decoupling of six-phase permanent magnet synchronous motor is established based on vector space decomposition,and its control system model is established.Motor signals in normal state and high resistance connection state are collected,and their energy distance features are extracted by wavelet packet decomposition,input to the back propagation neural network for offline training,and finally applied to sense development situation of high resistance connection state online under drastic conditions.Simulations are carried out based on Matlab,and the results show that the proposed strategy can effectively identify high resistance connection state,sensitively sense its development situation,send warning signals before the high resistance faults occur,and have certain robustness to drastic conditions.