电子测量与仪器学报

徐萌,周玉祥,徐海,张磊.基于改进粒子群算法的开关磁阻电机本体优化[J].电子测量与仪器学报,2023,37(4):131-141

基于改进粒子群算法的开关磁阻电机本体优化

Ontology optimization of switched reluctance motor based on improved particle swarm optimization algorithm

DOI：

中文关键词: 开关磁阻电机 Kriging 模型灵敏度分析粒子群算法多目标优化

英文关键词:switched reluctance motor Kriging model sensitivity analysis particle swarm optimization algorithm multiobjective optimization

基金项目:国家自然科学基金(51707195, 62173331)、民航安全能力建设基金(AADSA2021017)项目资助

作者	单位
徐萌	1. 中国民航大学电子信息与自动化学院
周玉祥	1. 中国民航大学电子信息与自动化学院
徐海	2. 中国民用航空沈阳航空器适航审定中心
张磊	2. 中国民用航空沈阳航空器适航审定中心

Author	Institution
Xu Meng	1. College of Electronic Information and Automation, Civil Aviation University of China
Zhou Yuxiang	1. College of Electronic Information and Automation, Civil Aviation University of China
Xu Hai	2. Shenyang Aircraft Airworthiness Certification Center of CAAC
Zhang Lei	2. Shenyang Aircraft Airworthiness Certification Center of CAAC

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中文摘要:

针对开关磁阻电机多变量、高非线性以及传统设计过程无法快速而准确获得最优方案的问题,提出一种基于 Kriging 模型和改进粒子群算法的参数优化策略。首先建立多目标优化模型,采用田口正交方法进行敏感性分析,依据灵敏度大小将优化变量分为两个子空间;其次为提高多目标粒子群算法的收敛速度和全局寻优精度,引入天牛须搜索算法中环境感应机制和遗传算法中交叉变异策略;最后建立 Kriging 模型,利用改进粒子群算法对两个子空间参数进行迭代寻优。实验结果表明,优化后的转矩脉动减少 23%,平均转矩提高 2. 3%,在大幅度减少转矩脉动情况下保持了较大平均转矩。结论是改进的粒子群算法与 Kriging 模型相结合策略适用于开关磁阻电机优化过程,可显著提高优化效率,保证求解精度。

英文摘要:

Aiming at the problem of multivariable and high nonlinearity of switched reluctance motors and the inability of traditional design process to obtain the optimal solution quickly and accurately, a parameter optimization strategy based on Kriging model and improved particle swarm algorithm is proposed. Firstly, a multi-objective optimization model is established, and Taguchi orthogonal method is used for sensitivity analysis, and the optimization variables are divided into two subspaces according to the sensitivity magnitude. Secondly, in order to improve the convergence speed and global optimization accuracy of multi-objective particle swarm optimization algorithm, the environmental induction mechanism in beetle antennae search algorithm and the crossover and mutation strategy in genetic algorithm are introduced. Finally, Kriging model is established and improved particle swarm algorithm is used to iteratively optimize the two subspace parameters. The experimental results show that the optimized torque ripple is reduced by 23% and the average torque is increased by 2. 3%, maintaining a large average torque with a significant reduction of torque ripple. The conclusion is that the combination of improved particle swarm optimization algorithm and Kriging model is suitable for optimization process of switched reluctance motor, which can significantly improve optimization efficiency and ensure solution accuracy.

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