| 引用本文: | 刘 晨,郭凯凯,张乃峰,李 聪.定子永磁型双凸极电机多参数多目标优化设计(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（1）：155-162 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 针对定子永磁型双凸极电机结构参数较多导致优化困难的问题,提出一种基于改进非支配排序遗传
算法Ⅱ的多参数多目标优化方法。 方法 根据多优化目标计算参数敏感度,通过设定权重系数计算综合敏感度,进
而根据综合敏感度将定子永磁型电机结构参数分为 3 层,其中,第一层和第二层结构参数为高敏感度结构参数,采
用常量基函数和二次有理核函数拟合高斯过程回归模型进行优化,第三层结构参数敏感度较低,使用单参数扫描
法进行优化,并且建立 6 个方案以比较不同权重系数和阈值对系统优化目标的影响。 结果 改进后的非支配排序遗
传算法Ⅱ相比传统算法具有更加优越的性能;优化后的定子永磁型双凸极电机的电磁转矩比初始结构提升了
15. 06%,齿槽转矩减少了 50. 9%,转矩脉动则从 20. 23%降低至 9. 45%。 结论 最后通过有限元仿真结果验证了所
提出多目标优化方法的可行性和有效性。 |
| 关键词: 多参数多目标优化 定子永磁型双凸极电机 非支配排序遗传算法Ⅱ 高斯过程回归模型 |
| DOI： |
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| Multi-parameter and Multi-objective Optimization Design of a Stator Permanent Magnet Doubly Salient Machine |
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LIU Chen GUO Kaikai ZHANG Naifeng LI Cong
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School of Electrical and Information Engineering Anhui University of Science and Technology Huainan 232001 Anhui
China
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| Abstract: |
| Objective To address the issue of optimization difficulties arising from the numerous structural parameters of
the stator permanent magnet PM doubly salient machine a multi-parameter and multi-objective optimization method
based on the improved non-dominated sorting genetic algorithm Ⅱ is proposed. Methods First the parameter sensitivity
was calculated according to multiple optimization objectives. Then the comprehensive sensitivity was computed through
the set weight coefficients. Finally the structural parameters of the stator PM doubly salient machine were divided into
three layers based on the comprehensive sensitivity. The structural parameters in the first and second layers were highly
sensitive and they were optimized by fitting a Gaussian process regression model with a constant basis function and a
quadratic rational kernel function. The structural parameters in the third layer were of lower sensitivity and were optimized
using the single-parameter scanning method. Six schemes were established to compare the effects of different weight
coefficients and thresholds on the system optimization objectives. Results The improved non-dominated sorting genetic
algorithm Ⅱ outperforms the traditional algorithm. After optimization the electromagnetic torque of the stator PM doubly
salient machine increased by 15. 06% the cogging torque decreased by 50. 9% and the torque ripple reduced from 20. 23% to 9. 45%. Conclusion The feasibility and effectiveness of the proposed multi-objective optimization method are
verified by the finite element simulation results. |
| Key words: multi-parameter and multi-objective optimization stator permanent magnet doubly salient machine nondominated sorting genetic algorithm Ⅱ Gaussian process regression model |
