Adaptive Flux and Torque Identification Scheme for Direct Torque Control System of Induction Motor

To improve the low-speed dynamic performance of induction motor in direct torque control (DTC), a novel method of stator resistance identification based on wavelet network is presented and the determination of wavelet network structure is discussed. The inputs of the WN are the current error and the change in the current error and the output of the WN is the stator resistance error. The improved least squares algorithm is used to fulfill the network structure and parameter identification. By the use of wavelet transform that accurately localizes the characteristics of a signal both in the time and frequency domains, the occurring instants of the stator resistance change can be identified by the multi-scale representation of the signal. Once the instants are detected, the accurate stator flux vector and electromagnetic torque are acquired by the parameter estimator, which makes the DTC applicable in the low region, optimizing the inverter control strategy. By detailed comparison between the wavelet and the typical backward-propagation neural network, the simulation results show that the proposed method can efficiently reduce the torque ripple and current ripple, superior to the BP neural network.