Multi-parameter identification of permanent magnet synchronous motor based on improved grey wolf optimization algorithm
Jinmei Jiang, Zhu Zhang · 2021
Considering 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 hybrid grey wolf optimization algorithm (IHGWO) is proposed. Firstly, Cat mapping and reverse learning strategy are used to generate the initial population, which can ensure the diversity of population and improve the convergence. Secondly, an improved sine cosine algorithm (SCA) is used to update the position of the leading wolf, so as to balance the global search and local development ability of IHGWO. Finally, Levy flight strategy is applied to update the optimal predation position of grey wolf group. It can improve the convergence accuracy and the ability to avoid trapping in local optimum. The full rank discrete model of PMSM is established on the d-q frame and the fitness function for parameter identification is given. The corresponding fitness value is obtained by comparing the output value of actual model with that of identification model. The IHGWO algorithm is compared with GWO and other two variants. The simulation and experimental results show that the IHGWO algorithm has the best accuracy, convergence speed and stability for parameter identification of PMSM.