Combined Random Forest and NSGA-II for Optimal Design of Permanent Magnet Arc Motor
Combined Random Forest and NSGA-II for Optimal Design of Permanent Magnet Arc Motor
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随机森林与 NSGA-II 相结合的永磁电弧电机优化设计
DOI：
10.1109/jestpe.2021.3049242
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发表时间：
2022-04
影响因子：
5.5
通讯作者：
Fang Shuhua
中科院分区：
文献类型：
--
作者：
Pan Zhenbao;Fang Shuhua
提出了一种双定子混合励磁永磁电弧电动机的优化设计方法。提出的优化方法结合机器学习算法随机森林（RF）和非支配排序遗传算法-II（NSGA-II）有助于实现高平均转矩，低转矩涟漪，高反电动势（EMF），和低总谐波失真的反电动势。首先介绍了直驱式混合永磁同步电动机的电机结构和工作原理。基于分析模型确定待优化的参数的选择。在此基础上，提出了一种基于变量重要性测度的灵敏度分析方法，以评估各结构参数对所选设计目标的影响。基于有限元分析（FEA）的DS-HE-PMAM模型的开发，以获得关于输入结构参数和输出设计目标的样本数据。基于样本数据，一个强大的机器学习算法RF被用来拟合输出设计目标和输入结构参数之间的函数关系。在此基础上，采用智能搜索算法NSGA-II对电机结构参数组合进行寻优，获得电机的最优性能。最后，对DS-HE-PMAM的初始模型和优化模型的电磁特性进行了对比分析，并通过有限元分析和样机实验验证了该优化方法的可行性和优越性。
This article presents an optimization design method for a double-stator hybrid excited permanent magnet arc motor (DS-HE-PMAM). The proposed optimization method combining the machine learning algorithm random forest (RF) and the nondominated sorting genetic algorithm-II (NSGA-II) contributes to achieving high average torque, low torque ripple, high back electromotive force (EMF), and low total harmonic distortion of the back EMF. First, the motor structure and working principle of the DS-HE-PMAM are illustrated. The selection of parameters to be optimized is determined based on an analytical model. Then, a variable importance measure-based new sensitivity analysis method is implemented to evaluate the influence of each structural parameter on the selected design objectives. The finite-element analysis (FEA)-based DS-HE-PMAM model is developed to obtain the sample data regarding input structural parameters and output design objectives. Based on the sample data, a powerful machine learning algorithm called RF is employed to fit the function relationship between output design objectives and input structural parameters. After that, an intelligent search algorithm named NSGA-II is introduced to search for the optimal solution to the structural parameters combination and obtain the optimal motor performances. Finally, the electromagnetic characteristics of the initial and optimized models of the DS-HE-PMAM are compared and analyzed, and both FEA and prototype experiments verify the feasibility and superiority of the proposed optimization method.