Parameter Identification Of Permanent Magnet Synchronous Motor Based On Improved Chaos Particle Swarm Optimization And Gray Wolf Optimization

With the development of new energy technology,new energy vehicles gradually appear in people’s daily life.Permanent Magnet Synchronous Motor(PMSM)is a kind of power Motor in new energy vehicle because of its simple structure,high efficiency,light weight and low failure rate.In addition,China has a very rich rare earth permanent magnet materials,which also provides an important material basis for the manufacturing technology of permanent magnet synchronous motor.On the other hand,PMSM has the characteristics of strong coupling,nonlinear and dynamic time-varying system.When PMSM is running,it will be affected by temperature,flux,current and so on.These changes will greatly affect the robustness of the system and the stability of various performance.However,in the process of using synchronous motor,high-precision identification and control of system parameters is an important condition to ensure the smooth operation of the control system.Therefore,this paper does the following work for the realization of high precision identification of PMSM parameters.(1)The mathematical models of PMSM are analyzed,and the relationship and transformation between three-phase static coordinate model and two-phase rotating coordinate model are discussed.Then,two control modes of permanent magnet synchronous motor are introduced,which are direct torque control and vector control.The principle and characteristics of the two control modes are compared and analyzed respectively.Finally,the zero-straight axis current control mode in vector control is analyzed,which provides a theoretical basis for establishing the identification model.(2)In order to solve the problem of low precision and slow speed of PMSM parameter identification with ordinary Particle Swarm Optimization(PSO)algorithm,an algorithm combining immune algorithm and chaos algorithm is proposed to optimize the parameters of PMSM.By comparing the simulation results of three Identification Algorithms,namely Particle Swarm Optimization(PSO),Chaos Particle Swarm Optimization(CPSO)and Immune Chaos Particle Swarm Optimization(ICPSO),the necessity and advantages of the improvement are proved.(3)The global search ability and the Shandong stick of the Grey Wolf Optimizer(GWO)have some shortcomings.Aiming at this problem,the differential evolution algorithm(DE)is used to optimize the grey Wolf Optimizer.This method integrates mutation,crossover and selection operations to ensure the diversity of the gray wolf population,so that the algorithm can find the optimal individual more effectively and quickly.In this paper,the Algorithm is applied to PMSM parameter identification,and the simulation results show that the improved algorithm has the advantages of faster identification speed and higher precision.