Computer-aided Experimental Identification of Line-start Permanent-magnet Synchronous-motor Parameters Based on Dynamic Electrical Processes*

Q1 Engineering
Vladimir Dmitrievskii;Aleksey Paramonov;Vadim Kazakbaev;Vladimir Prakht
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引用次数: 0

Abstract

Line-start permanent-magnet synchronous motors (LSPMSMs) are energy-efficient alternatives to induction motors. However, LSPMSMs are associated with synchronization difficulties when starting in cases of high inertia, heavy loads, or supply-voltage sags. Models with lumped parameters and parameter identification are used to study the starting processes. A novel approach for identifying the parameters of an LSPMSM is presented based on reduced experimental data that only include the recording of oscillograms of instantaneous currents and voltages when starting the LSPMSM from an idle state. The parameters of the lumped model are determined using the Nelder-Mead method while minimizing the root-mean-square error between the experimental and calculated waveforms. Multiple starts under different initial conditions are considered owing to uncertain initial conditions, and the initial conditions are excluded from the parameter space of the objective function. The proposed method is used to identify the parameters of a commercially available 0.55 kW, 1 500 r/min LSPMSM sample. The results obtained by modeling the starting processes of the LSPMSM using the identified parameters are similar to the experimental results.
基于动态电过程的起动永磁同步电机参数计算机辅助实验辨识*
线路启动永磁同步电动机(lspmsm)是感应电动机的节能替代品。然而,在高惯性、高负载或电源电压下降的情况下启动时,lspmsm与同步困难有关。采用集总参数模型和参数辨识模型对起动过程进行了研究。提出了一种基于简化实验数据的LSPMSM参数识别新方法,该实验数据仅包括从空闲状态启动LSPMSM时瞬时电流和电压的示波器记录。集总模型的参数采用Nelder-Mead方法确定,同时最小化了实验波形与计算波形之间的均方根误差。由于初始条件不确定,考虑了不同初始条件下的多次启动,并将初始条件从目标函数的参数空间中剔除。所提出的方法用于识别商用0.55 kW, 1 500 r/min LSPMSM样品的参数。利用所识别的参数对LSPMSM的启动过程进行建模,得到了与实验结果相似的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chinese Journal of Electrical Engineering
Chinese Journal of Electrical Engineering Energy-Energy Engineering and Power Technology
CiteScore
7.80
自引率
0.00%
发文量
621
审稿时长
12 weeks
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