基于遗传算法的内埋式永磁同步电机参数辨识方法

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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。

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	肖曦
	许青松
	王雅婷
	史宇超

关键词 ：内埋式永磁同步电机, 遗传算法, 参数辨识

Abstract：On account of the reverse salient pole characteristic and defects of the traditional parameter identification method, this article puts forward a parameter identification method based on genetic algorithms combine with the mathematical model of the motor. This method can identify the four parameters in same time such as the stator resistance, the d-axis inductance, the q-axis inductance and the permanent magnet flux. The signal used in the method are all can be directly detected the state variables so it can reduce the influence of the other disturbance on the motor parameters identification and improve the accuracy of the parameter identification. Simulation and experimental results show that the genetic algorithm to identify the parameters has a strong robustness and convergence. Four pending identification parameters can converge to the true value in a relatively short time and has a high accuracy no matter in the different speeds, loads and control strategies. It also overcomes the drawback of high requirements in the initial parameter values which in the commen genetic algorithm to identify.

Key words： Interior permanent magnet synchronous motor genetic algorithm parameter identification

收稿日期: 2013-09-10 出版日期: 2014-06-17

PACS:

TM341

基金资助:国家自然科学基金资助项目(51177084)

作者简介: 肖曦, 男, 1973年生, 博士, 副教授, 博士生导师, 研究方向为交流伺服电机高性能、高精度控制, 新能源发电与电力储能; 许青松, 男, 1987年生, 硕士研究生, 研究方向为电力电子与电力传动。

引用本文:

肖曦, 许青松, 王雅婷, 史宇超. 基于遗传算法的内埋式永磁同步电机参数辨识方法[J]. 电工技术学报, 2014, 29(3): 21-26. Xiao Xi, Xu Qingsong, Wang Yating, Shi Yuchao. Parameter Identification of Interior Permanent Magnet Synchronous Motors Based on Genetic Algorithm. Transactions of China Electrotechnical Society, 2014, 29(3): 21-26.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2014/V29/I3/21

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