Grey wolf optimizer for parameters identification of induction motor with improved model

According to the problems of inaccurate parameters estimation of the induction motor in high-performance control, a grey wolf optimizer was used to identify the parameters of the induction motor. Grey wolf optimizer is a new meta-heuristic. It is simple and flexible to implement, and has fewer parameters to tune. Considering that two typical dynamic mathematical models have different identification precision on different parameters, the improved identification model of the induction motor was proposed. Compared with typical model, simulation results show that the proposed model obviously improves the identification performance of resistances especially stator resistance, verifying the validity of improved model. The algorithm was compared with particle swarm optimization and genetic algorithm for parameters identification of the induction motor with the improved model. Experimental results show that grey wolf optimizer has higher identification precision, demonstrating that parameters identification of the induction motor based on this algorithm is feasible.