Research on Multi-Parameter Identification Method of Permanent Magnet Synchronous Motor

被引：0
|
作者：
Liu X. [1 ]
Hu W. [1 ]
Ding W. [1 ]
Xu H. [1 ]
Zhang Y. [1 ]
机构：
[1] School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2020年 / 35卷 / 06期
关键词：
Chaotic mutation; Initial paramter optimization; Niche; Parameter identification; Particle swarm optimization; Permanent magnet synchronous motor;
D O I：
10.19595/j.cnki.1000-6753.tces.190122
中图分类号：
学科分类号：
摘要：
Aiming at the difficulty of multi-parameter identification of permanent magnet synchronous motor (PMSM), this paper proposed a chaotic mutation niche particle swarm optimization algorithm with initial parameter optimization (NCOPSO), and designed a full-rank mathematical system with five parameters (stator winding resistance, stator cross-axis and direct-axis inductance, permanent magnet flux, moment of inertia) to be identified. Firstly, the particle swarm optimization was used to optimize the three initial parameters (inertia coefficient ω, learning factor c1, c2) of the basic particle swarm optimization algorithm. Then the niche strategy was applied to the optimized particle swarm: a niche population centering on the particles with small adaptive value changes in successive iterations was constructed. Finally, the chaotic mutation strategy is used: in each iteration process, a chaotic sequence was generated based on the optimal particles of each niche group. The optimal particles in the sequence were randomly replaced by a certain particle of the current niche population, and the worst particle of the niche mirror population was initialized at the same time. The feasibility and accuracy of the algorithm were verified by motor simulation and experiment. © 2020, Electrical Technology Press Co. Ltd. All right reserved.
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页码：1198 / 1207
页数：9
相关论文
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