基于改进灰狼优化算法的永磁同步电机参数辨识  

PMSM Parameter Identification Based on Improved Grey Wolf Optimization Algorithm

作　　者：李伟[1] 杜昭平[1] LI Wei;DU Zhao-ping(School of Electronic Information,Jiangsu University of Science and Technology,Zhenjiang Jiangsu 212003,China)

机构地区：[1]江苏科技大学电子信息学院,江苏镇江212003

出　　处：《组合机床与自动化加工技术》2020年第7期113-117,共5页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金项目(61304249,61374063)。

摘　　要：针对永磁同步电机中参数辨识精度低与速度慢的问题,提出了一种基于改进灰狼优化算法的参数辨识方法。首先采用Tent映射反向学习策略对种群初始化,增强初始种群的多样性;其次,设计了一种非线性变化的收敛因子;最后,采用Levy飞行策略对算法寻优过程中位置更新方式进行变异操作,以提高算法跳出局部最优的能力,降低了算法误收敛的可能性。仿真实验表明,改进灰狼优化算法对永磁同步电机中的参数精确度更高,辨识速度更快。Aiming at the problem of low precision and slow speed of parameter identification in permanent magnet synchronous motor, a parameter identification method based on improved grey wolf optimization algorithm is proposed. Firstly, the Tent mapping opposition-based learning strategy is used to initialize the population and enhance the diversity of the initial population. Secondly, a convergence factor of nonlinear variation is designed. Finally, using Levy flight strategy to mutate the location update method in the algorithm optimization process can improve the ability of the algorithm to jump out of local optimum and reduce the possibility of algorithmic convergence. The simulation experiments show that the improved gray wolf optimization algorithm has higher accuracy and faster identification speed in the permanent magnet synchronous motor.

关 键 词：永磁同步电机 参数辨识 灰狼优化算法 TENT映射 反向学习 

分 类 号：TH162[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]