永磁同步电机在线参数辨识研究  

Research on online parameter identification of permanent magnet synchronous motors

作　　者：郗建国 冯毅潇 赵宾鹏 高建平 XI Jianguo;FENG Yixiao;ZHAO Binpeng;GAO Jianping(College of Vehicle and Traffic Engineering,Henan University of Science and Technology,Luoyang 471003,China)

机构地区：[1]河南科技大学车辆与交通工程学院,河南洛阳471003

出　　处：《重庆理工大学学报（自然科学）》2023年第7期289-296,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：中原科技创新领军人才项目(224200510014)。

摘　　要：针对基本粒子群算法在永磁同步电机参数辨识过程中存在辨识时间长、收敛速度慢等问题,提出混沌映射与信息传递相结合的混沌遗传粒子群算法(CHPSO)对永磁同步电机进行参数在线辨识。通过混沌映射产生混沌粒子,并与前一次参数辨识结果相结合,生成初始化种群,再引入动态惯性权重系数,提高粒子多样性。采用分步辨识和循环更新的方法,解决参数辨识的欠秩问题。仿真结果表明:该算法对电机参数进行辨识的结果偏差分别为定子电阻1.32%,磁链1.08%,d轴电感0.92%,q轴电感1.16%。台架实验证明了辨识方案的有效性。Aiming at the problems of long recognition time and slow convergence speed in the parameter identification process of permanent magnet synchronous motors of the basic particle swarm algorithm,this paper proposes a chaotic genetic particle swarm algorithm(CHPSO)combining chaotic mapping and information transmission to identify the parameters of permanent magnet synchronous motors online.The algorithm generates chaotic particles through chaos mapping,combines with the previous parameter identification results to generate an initialized population,and then introduces dynamic inertia weight coefficients to improve particle diversity.At the same time,step-by-step identification and cyclic updating methods are adopted to solve the problem of under-ranking parameter identification.The simulation shows that the deviations of the algorithm in identifying the motor parameters are 1.32%stator resistance,1.08%flux linkage,0.92%d-axis inductance and 1.16%q-axis inductance respectively.Finally,the effectiveness of the identification scheme is proved by bench experiments.

关 键 词：永磁同步电机 参数在线辨识 粒子群算法 混沌映射 

分 类 号：TM351[电气工程—电机]