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.
刘朝华, 李小花, 周少武, 刘侃. 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[J]. 电工技术学报, 2014, 29(5): 118-126.
Liu Zhaohua, Li Xiaohua, Zhou Shaowu, Liu Kan. Comprehensive Learning Particle Swarm Optimization Algorithm based on Immune Mechanism for Permanent Magnet Synchronous Motor Parameter Identification. Transactions of China Electrotechnical Society, 2014, 29(5): 118-126.
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