基于双模型的递推最小二乘永磁同步直线电机电气参数在线辨识
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福建省新能源发电与电能变换重点实验室(福州大学)

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Online identification of electrical parameters of dual-model recursive least squares permanent magnet synchronous linear motor
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Fujian Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University)

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    摘要:

    为了实现永磁同步直线电机(permanent magnet synchronous linear motor,PMSLM)高精度的多电气参数在线辨识,本文基于递推最小二乘理论,提出一种变遗忘因子双模型在线辨识算法。首先根据电机的dq轴电压方程分别建立了辨识定子电阻、永磁体磁链的模型一和辨识q轴电感、d轴电感的模型二,并将两个辨识模型循环结合。其次,基于上述模型,采用变遗忘因子的递推最小二乘算法实现了PMSLM的电气参数在线辨识,并保证辨识的收敛速度和准确性;同时,针对PMSLM运行时因功率开关非理想因素导致的误差电压进行补偿,进一步提高了辨识的精准度。最后,仿真和实验结果证明了该辨识算法的有效性,且具有收敛速度快、辨识结果精度高、多工况适用等优点。

    Abstract:

    In order to realize the high-precision online identification of multiple electrical parameters of permanent magnet synchronous linear motor (PMSLM), based on the recursive least squares theory, this paper proposes an online identification algorithm of variable forgetting factor dual models. Firstly, according to the dq-axis voltage equation of the motor, the first model for identifying stator resistance and permanent magnet flux linkage and the second model for identifying q-axis inductance and d-axis inductance are established respectively, and the two identification models are combined cyclically. Secondly, based on the above model, the recursive least squares algorithm with variable forgetting factor is used to realize the online identification of the electrical parameters of the PMSLM, and to ensure the convergence speed and accuracy of the identification. The error voltage is compensated to further improve the accuracy of identification. Finally, the simulation and experimental results demonstrate the effec-tiveness of the identification algorithm, and it has the advantages of fast convergence speed, high accuracy of identification results, and mul-ti-working conditions.

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  • 收稿日期:2022-05-08
  • 最后修改日期:2022-08-25
  • 录用日期:2022-07-19
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