基于珊瑚礁算法的永磁同步电机参数辨识

Coral Reefs Optimization for Solving Parameter Identification in Permanent Magnet Synchronous Motor

永磁同步电机参数的高精度辨识是进行控制器设计的基础。针对传统的粒子群算法以及最小二乘法在辨识电机参数时速度慢,误差大,可辨识参数少的缺点,提出了将珊瑚礁算法应用于求解永磁同步电机多参数辨识的问题。在dq坐标系下建立永磁同步电机参数辨识模型,将珊瑚礁算法、粒子群算法、和最小二乘法应用于求解永磁同步电机参数辨识问题,并在Matlab/Simulink中进行了对比验证。实验结果表明珊瑚礁算法能同时辨识定子电阻、d轴电感、q轴电感、转子磁链等电磁参数并且具有较好的收敛性能。

High accuracy identification of parameters in permanent magnet synchronous motor（PMSM） is the basis of controller design. According to the drawbacks of slow speed, big error, and small number of parameters in classical particle swarm optimization（PSO） and least square method, Coral Reefs Optimization（CRO） was proposed to solve the parameter identification problem in PMSM. In order to improve the identification accuracy, the parameter setting in CRO was adjusted. The mathematical model of PMSM in dq coordinate system was established, CRO, PSO and RLS were applied to identify parameters in PMSM, and were verified in Matlab/Simulink for comparison. The simulation results indicate that CRO algorithm is able to improve the identification accuracy of stator resistance, d-axis inductance, q-axis inductance, rotor flux and guarantee the performance improvement in PMSM.