作者：李英春, 侯金明, 王培瑞

作者单位：陕西科技大学电气与控制工程学院，陕西 西安 710021

关键词：永磁同步电机;模型参考自适应;扩展卡尔曼滤波;参数辨识

摘要：

为解决永磁同步电机多参数辨识过程中辨识精度较小、收敛速度慢的问题，设计一种基于扩展卡尔曼滤波的在线参数辨识方法。首先，结合永磁同步电机数学模型，采用Popov超稳定理论设置自适应律，经过模型参考自适应算法辨识出电机实时转动惯量；其次，将转动惯量送入扩展卡尔曼滤波模块，最终实现电机电感和磁链的在线辨识，引入的转动惯量能够实时更新修正电感与磁链的辨识结果。Matlab仿真表明：所提出的方法收敛速度快、误差较小且稳定性好，在负载变化时能够实时修正辨识结果。所设计系统辨识误差小于1%，验证其可行性。

PMSM online parameter identification method based on improved extended Kalman filter
LI Yingchun, HOU Jinming, WANG Peirui
College of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
Abstract: In order to solve the problems of low identification accuracy and slow convergence speed in the process of multi-parameter identification of permanent magnet synchronous motors, an online parameter identification method based on extended Kalman filter is designed. First, combined with the mathematical model of the permanent magnet synchronous motor, the Popov ultra-stable theory is used to set the adaptive law, and the real-time moment of inertia of the motor is identified through the model reference adaptive algorithm; secondly, the moment of inertia is sent to the extended Kalman filter module to finally realize the motor inductance and on-line identification of flux linkage, the introduced moment of inertia can update and correct the identification results of inductance and flux linkage in real time. Matlab simulation shows that the proposed method has fast convergence speed, small error and good stability, and it can correct the identification results in real time when the load changes. The experimental results show that the identification error of the designed system is less than 1%, which verifies its feasibility.
Keywords: permanent magnet synchronous motor;model reference adaptive;extended Kalman filter;parameter identification
2022, 48(11):47-53  收稿日期: 2021-06-13;收到修改稿日期: 2021-08-06
基金项目: 国家自然科学基金资助项目（59493300）；教育部博士点基金资助项目（9800462）
作者简介: 李英春（1969-），男，陕西咸阳市人，高级工程师，硕导，研究方向为电机伺服控制、电力系统继电保护
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