采用新型遗传算法的永磁同步电机参数辨识

Parameter Identification of Interior Permanent Magnet Synchronous Motor Based on New Genetic Algorithm

针对内置式永磁同步电机(IPMSM)所具备的凸极特性以及传统遗传算法参数辨识的缺陷,提出一种基于局部搜索混合遗传算法(Is-hGA)的参数辨识方法。Is-hGA算法是爬山方法与遗传算法的结合,该方法可同时辨识定子电阻、d轴电感、q轴电感和永磁通链4个参数。一般来说,IPMSM的参数辨识问题可转化为寻找最优解问题。在所提出的混合优化方法中,由遗传算法执行全局搜索任务,爬山算法进行局部搜索,可改善遗传算法局部搜索能力较差的问题,大大节省了计算时间。实验结果表明,采用Is-hGA算法的IPMSM参数辨识的最小辨识精度分别达到了6.82%、3.62%、2.32%和2.12%。与传统遗传算法相比,其搜索效率及辨识精度得到大幅提高。因此,基于Is-hGA算法的参数辨识不仅可减少迭代次数、节省计算时间,而且具有很高的参数辨识精度。

Aiming at the anti salient characteristics of interior permanent magnet synchronous motor(IPMSM)and the defects of traditional genetic algorithm parameter identification method,a parameter identification method based on local search-based hybrid genetic algorithm(Is-hGA)is proposed.The Is-hGA is a combination of hill-climbing method and genetic algorithm.This method can identify four parameters of stator resistance,d-axis inductance,q-axis inductance and permanent magnet flux linkage at the same time.Generally,the parameter identification problem of IPMSM can be transformed into a problem to find the optimal solution.In the proposed hybrid optimization method,the global search task is performed by GA,while the hill-climbing algorithm is used for local search.In this way,the problem of poor local search ability of GA can be improved,but also the computing time can be saved greatly.The experimental results show that the minimum identification accuracy of the IPMSM parameter identification using the Is-hGA algorithm reaches 6.82%,3.62%,2.32%,and 2.12%,respectively.Compared with the traditional genetic algorithm,the search efficiency and identification accuracy are greatly improved.Therefore,the parameter recognition based on the Is-hGA algorithm can not only reduce the number of iterations,save computational time,but also have a high parameter recognition accuracy.