基于混合粒子群优化算法的永磁同步电机参数辨识  

Parameter Identification of Permanent Magnet Synchronous Motor Based on Hybrid Particle Swarm Optimization Algorithm

作　　者：李强 周士贵 曹凤斌 俞力豪 张顺杰 张可程 LI Qiang;ZHOU Shigui;CAO Fengbin;YU Lihao;ZHANG Shunjie;ZHANG Kecheng(College of Engineering,Qufu Normal University,Rizhao 276800,China;Rizhao Dongfang Electric Machinery Co.,Ltd.,Rizhao 276800,China)

机构地区：[1]曲阜师范大学工学院,日照276800 [2]日照东方电机有限公司,日照276800

出　　处：《微特电机》2024年第6期55-61,共7页Small & Special Electrical Machines

基　　金：山东省自然科学基金面上项目(ZR2021ME017);科技型中小企业创新能力提升工程(2021TSGC1429)。

摘　　要：永磁同步电机(PMSM)在实际应用中是一种强非线性系统,运行过程中由于温度和磁饱和等因素造成电机参数发生变化,进而影响PMSM控制效果。为了提高PMSM的控制性能,在对永磁同步电机无差拍电流预测控制系统深入分析的基础上,提出了一种基于模型参考自适应(MRAS)和遗传粒子群(GAPSO)混合优化的在线参数辨识算法。该算法通过MRAS初步辨识PMSM的电气参数,其辨识结果为粒子群寻优提供方向;同时改进遗传算法中交叉变异机制,将初步辨识结果引入遗传粒子群算法中进一步优化。仿真实验结果表明,MRAS-GAPSO算法所有参数在迭代50次内实现了较高精度的辨识,且辨识相对误差均不超过1%,验证了算法在不同工况下的可行性,实现了参数的在线精确辨识。Permanent magnet synchronous motor(PMSM)is a strongly nonlinear system in practical application.The parameters of PMSM will change due to temperature and magnetic saturation,which can affect the control effect of PMSM.An online parameter identification algorithm was proposed based on hybrid optimization of model reference adaptive system(MRAS)and genetic particle swarm optimization(GAPSO).The electrical parameters of PMSM were initially identified by MRAS,and the identification results provided direction for particle swarm optimization.Subsequently,the crossover mutation mechanism of the genetic algorithm was improved,and the preliminary identification results were introduced into the genetic particle swarm optimization algorithm for further optimization.The simulation results show that all parameters identified by the MRAS-GAPSO algorithm achieve high precision identification within 50 iterations,with a relative error of less than 1%.The simulation results also demonstrate the algorithm’s high convergence performance and its ability to accurately identify parameters in real-time.

关 键 词：永磁同步电机 参数辨识 无差拍电流预测 模型参考自适应 粒子群算法 

分 类 号：TM351[电气工程—电机]