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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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

Received: 30 June 2018 Published: 15 February 2019

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TM351

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	Zhang Liwei
	Zhang Peng
	Liu Yuefeng
	Zhang Chao
	Liu Jie

Cite this article:

Zhang Liwei,Zhang Peng,Liu Yuefeng等. Parameter Identification of Permanent Magnet Synchronous Motor Based on Variable Step-Size Adaline Neural Network[J]. Transactions of China Electrotechnical Society, 2018, 33(zk2): 377-384.

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

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