A novel integration algorithm for flux and torque estimation of induction motor in low speed operation

A novel method of stator resistance identification based on wavelet network is presented and the determination of wavelet network structure is discussed in order to improve the low-speed dynamic performance of induction motor in direct torque control. The current error and the change in the current error is the inputs of the wavelet network and the stator resistance error is the output of the wavelet network. The network structure and parameter identification is fulfilled by the evolutionary algorithm. The characteristics of a signal both in the time and frequency domains are localized accurately by means of wavelet transform, so the occurring instants of the stator resistance change can be identified by the multi-scale representation of the signal. The accurate stator flux vector and electromagnetic torque are acquired by the parameter estimator once the instants are detected, in this way the direct torque control can be applicable in the low region and the inverter control strategy can be optimized. The simulation results show that the wavelet-based method can efficiently reduce the torque ripple and current ripple and is superior to the BP neural network.