Accurate and Efficient Surrogate Model-Assisted Optimal Design of Flux Reversal Permanent Magnet Arc Motor
Accurate and Efficient Surrogate Model-Assisted Optimal Design of Flux Reversal Permanent Magnet Arc Motor
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DOI：
10.1109/tie.2022.3215444
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发表时间：
2023
影响因子：
7.7
通讯作者：
Zhenbao Pan;S. Fang;Hui Wang;Yuxiang Zhong
中科院分区：
文献类型：
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作者：
Zhenbao Pan;S. Fang;Hui Wang;Yuxiang Zhong
准确、高效的代理模型在电机优化设计中发挥着重要作用。提出了一种基于机器学习算法的磁通反转永磁电弧电动机代理建模方法。具体而言，首先说明了电机的拓扑结构和FR-PMAM的优化设计。在此基础上，建立了永磁体磁动势-磁导模型，用于FR-PMAM结构参数的选择。在此之后，有限元分析（FEA）模型的FR-PMAM计算，以获得样本数据。首次将机器学习算法随机森林引入电机优化设计领域，评价各参数对设计目标的贡献。在样本数据的基础上，创新性地采用了一种高效的机器学习算法CatBoost，建立了精确高效的FR-PMAM代理模型，该模型能够捕捉输出目标与输入参数之间的复杂函数关系。然后采用保留精英策略的改进遗传算法对FR-PMAM的结构参数进行优化。最后，制作了FR-PMAM样机。有限元分析和实验结果验证了代理建模方法的有效性。
An accurate and efficient surrogate model plays a significant role in the optimal design of electrical motors. This article presents an accurate and efficient surrogate modeling method for a flux reversal permanent magnet arc motor (FR-PMAM) based on machine learning algorithm. Specifically, the motor topology and the optimized design of the FR-PMAM are firstly illustrated. Then, the PM magnetic motive force-permeance model is developed to select structural parameters of FR-PMAM. After that, a finite-element analysis (FEA) model of FR-PMAM is calculated to obtain sample data. A machine learning algorithm random forest is introduced into the field of motor optimal design for the first time to evaluate the contribution of each parameter to the design objectives. Based on the sample data, an efficient machine learning algorithm CatBoost is innovatively employed to establish the accurate and efficient surrogate model of the FR-PMAM that can capture the complex function relationship between output objectives and input parameters. Subsequently, an improved genetic algorithm with the elitist reservation strategy is adopted to obtain the optimal combination of the structural parameters of FR-PMAM. Finally, a prototype of the FR-PMAM is manufactured. The FEA and experimental results validate the effectiveness of the investigated surrogate modeling method.