基于混沌果蝇算法的SRM多目标协同优化研究

doi:10.16182/j.issn1004731x.joss.201807026

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (7): 2640-2647.doi: 10.16182/j.issn1004731x.joss.201807026

基于混沌果蝇算法的SRM多目标协同优化研究

张小平¹, 饶盛华¹, 张铸², 赵轩¹

1. 湖南科技大学海洋矿产资源探采装备与安全技术国家地方联合工程实验室,湖南湘潭 411201;
2. 湖南科技大学信息与电气工程学院,湖南湘潭 411201

收稿日期:2017-01-05 出版日期:2018-07-10 发布日期:2019-01-08
作者简介:张小平(1966-),男,湖南株洲,博士,教授,研究方向为电力电子与电力传动。
基金资助:
国家自然科学基金(51477047,61503132),湖南省自然科学湘潭联合基金(2016JJ5026),湖南省研究生科研创新项目(CX2016B604)

Study on Multi-objective Collaborative Optimization of Switched Reluctance Motor Based on Chaos Fruit Fly Optimization Algorithm

Zhang Xiaoping¹, Rao Shenghua¹, Zhang zhu², Zhao xuan¹

1. National-Local Joint Engineering Laboratory of Marine Mineral Resources Exploration Equipment and Safety Technology,Hunan University of Science and Technology, Xiangtan 411201, China;
2. College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China

Received:2017-01-05 Online:2018-07-10 Published:2019-01-08

摘要/Abstract

摘要： 开关磁阻电机(SRM)因传统设计方法过程繁琐、专业性要求高且难以获得最优方案,提出一种基于混沌果蝇算法的SRM多目标协同优化设计方法。利用传统设计方法得到SRM的初始方案,有限元分析对其性能核算并获得样本数据集,采用极限学习机(ELM)对样本数据进行训练得到SRM的非参数模型和混沌果蝇算法对其进行优化,对其优化效果进行了仿真验证。结果表明：采用混沌果蝇算法优化能有效提高转矩波动系数与效率指标,且具有参数设置少、收敛速度快且不易陷入局部最优解等优点,具有较好的应用价值。

关键词: 开关磁阻电机, 多目标协同优化, 混沌果蝇算法, 极限学习机

Abstract: Switched reluctance motor (SRM) is difficult to get the best solution due to its complicated process and high professional requirement. Therefore, a collaborative multi-objective optimization method based on chaotic fruit fly algorithm is proposed in the paper. The initial solution is obtained by traditional design method, and the performance is verified by finite element analysis (FEA). The extreme learning machine (ELM) is applied to obtain the non-parameter model of SRM based on the sample data from FEA. The chaotic fruit fly algorithm is proposed for design optimization. Simulation results demonstrate that better coefficient of torque ripple and efficiency can be obtained, and it has the advantages of fewer parameters, faster convergence and is not easy to fall into local optimal solution. Moreover, better application value can be achieved.

Key words: SRM, multi-objective collaborative optimization, chaos fruit fly optimization algorithm, extreme learning machine

中图分类号:

TM352

张小平, 饶盛华, 张铸, 赵轩. 基于混沌果蝇算法的SRM多目标协同优化研究[J]. 系统仿真学报, 2018, 30(7): 2640-2647.

Zhang Xiaoping, Rao Shenghua, Zhang zhu, Zhao xuan. Study on Multi-objective Collaborative Optimization of Switched Reluctance Motor Based on Chaos Fruit Fly Optimization Algorithm[J]. Journal of System Simulation, 2018, 30(7): 2640-2647.

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基于混沌果蝇算法的SRM多目标协同优化研究

Study on Multi-objective Collaborative Optimization of Switched Reluctance Motor Based on Chaos Fruit Fly Optimization Algorithm

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