免疫协同微粒群进化算法的永磁同步电机多参数辨识模型方法

刘朝华; 章兢; 李小花; 张英杰

doi:10.3724/SP.J.1004.2012.01698

免疫协同微粒群进化算法的永磁同步电机多参数辨识模型方法

doi: 10.3724/SP.J.1004.2012.01698

1.
湖南科技大学信息与电气工程学院湘潭 411201;
2.
湖南大学电气与信息工程学院长沙 410082;
3.
湖南大学信息科学与工程学院长沙 410082

详细信息

通讯作者:
刘朝华

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出版历程
- 收稿日期: 2011-02-24
- 修回日期: 2011-10-13
- 刊出日期: 2012-10-20

Immune Co-evolution Particle Swarm Optimization for Permanent Magnet Synchronous Motor Parameter Identification

1.
School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201;
2.
College of Electrical and Information Engineering, Hunan University, Changsha 410082;
3.
School of Information Science and Engineering, Hunan University, Changsha 410082

摘要

摘要: 针对永磁同步电机多参数辨识问题,提出一种基于免疫协同微粒群进化(Immune co-evolution particle swarm optimization, ICPSO) 算法的永磁同步电机(Permanent magnet synchronous motor, PMSM) 多参数辨识方法.算法由记忆种群与若干个普通种群构成, 在进化过程中普通种群中优秀个体进入记忆库种群.普通种群内部通过精英粒子保留、免疫网络以及柯西变异等混合策略共同产生新个体,个体极值采用小波学习加快收敛速度,免疫克隆选择算法对记忆库进行精细搜索,迁移机制实现了整个种群的信息共享与协同进化.永磁同步电机参数辨识结果表明该方法不需要知道电机设计参数先验知识,能够有效地辨识电机电阻、 dq轴电感与转子磁链,且能有效追踪该参数变化值.
- 人工免疫系统 /
- 粒子群优化 /
- 协同进化 /
- 永磁同步电机 /
- 参数辨识
Abstract: Aiming at the problem of permanent magnet synchronous motor (PMSM) multiple parameter identification, a novel parameter identification approach to PMSM based on immune co-evolution particle swarm optimization (ICPSO) algorithm is proposed. The proposed ICPSO consists of one memory subpopulation and several normal subpopulations. In each generation of the algorithm, the best individual of each normal subpopulation will be memorized into the memory population. A hybrid method, such as elitist reservation, immune network, cauchy mutation, etc., which creates new individuals by using three different operators, is proposed to ensure the diversity of all the normal subpopulations. Furthermore, a simple wavelet learning operator is employed for accelerating the convergence speed of pbest. The immune clonal selection operator is employed for optimizing the memory population while the migration scheme is employed for the information exchange between memory and normal subpopulations. Its performance is further verified by its application in multi-parameter estimation of permanent magnet synchronous machines, 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. In addition, it can also effectively track the varied parameter.
- Aartificial immune system (AIS) /
- particle swarm optimization (PSO) /
- co-evolutionary /
- permanent magnet synchronous motors (PMSM) /
- parameter identification

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参考文献(1)

[1]

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