Multi-parameter identification of permanent magnet synchronous motor based on improved grey wolf optimization algorithm

In view of the deficiency of the parameter identification algorithm of permanent magnet synchronous motor(PMSM) , such as low identification accuracy and difficulty in identifying multiple parameters at the same time, an improved grey wolf optimization algorithm based on normal cloud model(CGWO) is proposed. Firstly, Fuch mapping and reverse learning strategy were adopted to generate diverse initial population. Secondly, a nonlinear decreasing convergence factor updating formula was adopted to balance the global search and local development ability of the algorithm. Finally, the normal cloud model was a-dopted to update the position and develop the depth of the grey wolf group. At the same time, the adaptive adjustment of the cloud model parameters was used to enhance the local optimization ability, which can improve the problem that the traditional grey wolf optimization algorithm is easy to fall into local optimization leading to the reduction of accuracy. The performance of CGWO algorithm was evaluated by benchmark test function, and a full rank discrete model of PMSM was established on the d - q frame. Given the fitness function, the corresponding fitness value was obtained by comparing the output value of actual model with that of identification model, and then the parameter identification was realized by combining with the CGWO algorithm. The simulation and experiment show that the improved CGWO algorithm has better accuracy, convergence and stability for PMSM parameter identification. © 2022 Editorial Department of Electric Machines and Control. All rights reserved.