面向永磁同步电机参数辨识的免疫完全学习型粒子群算法

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Abstract The engineering reality permanent magnet synchronous motor system is a kind of strong nonlinear dynamic system. It is hard to obtain high precision parameters estimated value using the conventional method. Comprehensive learning particle swarm optimization (CLPSO) is a new global optimization algorithm which can solve multi-modal problem efficiently. Meanwhile, the artificial immune system (AIS) has strong local search ability. In this paper, a novel algorithm named comprehensive learning particle swarm optimization evolutionary computing model based on immune mechanism is proposed, which combined CLPSO with artificial immune system (AIS). Finally, the proposed method is further verified by its application in permanent magnet synchronous machines multi-parameter identification and modeling, which shows that its performance is much better than other PSOs in simultaneously estimating the machine dq-axis inductances, stator winding resistance and rotor flux linkage. Additionally, it is also effective tracking the varied parameter.

Key words： Artificial immune system (AIS) particle swarm optimization (PSO) comprehensive learning permanent magnet synchronous motor (PMSM) parameter identification

Received: 13 August 2012 Published: 05 November 2014

PACS:	TP18
	TM85

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	Liu Zhaohua
	Li Xiaohua
	Zhou Shaowu
	Liu Kan

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Liu Zhaohua,Li Xiaohua,Zhou Shaowu等. Comprehensive Learning Particle Swarm Optimization Algorithm based on Immune Mechanism for Permanent Magnet Synchronous Motor Parameter Identification[J]. Transactions of China Electrotechnical Society, 2014, 29(5): 118-126.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I5/118

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