基于遗传算法的内埋式永磁同步电机参数辨识方法

发布时间：2019-04-01 17:29

【摘要】：针对内埋式永磁同步电机的反凸极特性及传统参数辨识方法存在的缺陷,结合电机的数学模型提出了一种基于遗传算法的参数辨识方法,该方法能同时辨识定子电阻、d轴电感、q轴电感和永磁体磁链四个参数。该方法所用的信号均为可直接检测的状态变量,从而减少了其他干扰对电机参数辨识的影响,提高了参数辨识的准确性。仿真和实验结果表明,利用遗传算法进行参数辨识鲁棒性强、收敛性好,在不同的转速、不同的负载以及不同的控制策略下,四个待辨识参数也能够在较短的时间内收敛到真实值,具有较高的精度,同时也克服了一般遗传算法对辨识参数初始值要求高的缺点。
[Abstract]:Aiming at the inverse salient characteristics of embedded permanent magnet synchronous motor (PMSM) and the defects of traditional parameter identification methods, a parameter identification method based on genetic algorithm (GA) is proposed, which can identify stator resistance at the same time, combined with the mathematical model of motor. D-axis inductance, Q-axis inductance and permanent magnet flux four parameters. The signals used in this method are all state variables that can be directly detected, thus reducing the influence of other disturbances on the identification of motor parameters and improving the accuracy of parameter identification. The simulation and experimental results show that the genetic algorithm is robust and convergent for parameter identification. Under different rotational speed, different load and different control strategies, the genetic algorithm has the advantages of robustness and convergence. The four parameters to be identified can converge to the real value in a relatively short time, which has high accuracy. At the same time, it also overcomes the shortcomings of the general genetic algorithm in requiring high initial values of the identification parameters.
【作者单位】：清华大学电机系电力系统及发电设备安全控制和仿真国家重点实验室;长园深瑞继保自动化有限公司;江西省电力公司电力经济技术研究院;
【基金】：国家自然科学基金资助项目(51177084)
【分类号】：TM341

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