基于变步长Adaline神经网络的永磁同步电机参数辨识

doi:10.19595/j.cnki.1000-6753.tces.L80266

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摘要为了提高永磁同步电机在线参数辨识结果的收敛速度并减小稳态误差,首先对考虑逆变器非线性因素电压补偿的表贴式永磁同步电机电压方程进行变换,从而消除误差电压的影响。在此基础上,提出一种变步长自适应线性（Adaline）神经网络算法,对表贴式永磁同步电机参数进行在线辨识。实验结果表明,所提方法能有效减小定子电阻、电感、转子磁链辨识结果的稳态误差,同时提高辨识结果的收敛速度。

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	张立伟
	张鹏
	刘曰锋
	张超
	刘杰

关键词 ：表贴式永磁同步电机, 变步长Adaline神经网络, 参数辨识, 稳态误差

Abstract：In order to improve the convergence speed of the online parameter identification results of the permanent magnet synchronous motor and reduce the steady-state error, firstly, the voltage equation of the surface-mount permanent magnet synchronous motor (SPMSM) considering the voltage compensation of the nonlinear factor of the inverter is transformed to eliminate the influence of the error voltage. On this basis, this paper proposed a variable step size Adaline neural network algorithm to identify the parameters of surface-mount permanent magnet synchronous motor. The experimental results show that the proposed method can effectively reduce the steady-state error of stator resistance, inductance and rotor flux identification, and improve the convergence speed of the identification results.

Key words： Surface-mounted permanent magnet synchronous motor variable-step Adaline neural network parameter identification steady-state error

收稿日期: 2018-06-30 出版日期: 2019-02-15

PACS:

TM351

基金资助:中央高校基本科研业务费项目资助（2018YJS151）

通讯作者: 张立伟男,1977年生,副教授,博士生导师,研究方向为电机及其控制。E-mail: lwzhang@bjtu.edu.cn

作者简介: 张鹏男,1994年生,硕士研究生,研究方向为永磁同步电机控制。E-mail: 16121574@bjtu.edu.cn

引用本文:

张立伟, 张鹏, 刘曰锋, 张超, 刘杰. 基于变步长Adaline神经网络的永磁同步电机参数辨识[J]. 电工技术学报, 2018, 33(zk2): 377-384. Zhang Liwei, Zhang Peng, Liu Yuefeng, Zhang Chao, Liu Jie. Parameter Identification of Permanent Magnet Synchronous Motor Based on Variable Step-Size Adaline Neural Network. Transactions of China Electrotechnical Society, 2018, 33(zk2): 377-384.

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https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.L80266 https://dgjsxb.ces-transaction.com/CN/Y2018/V33/Izk2/377

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