基于深度神经网络的永磁直线电机仿真与优化

doi:10.16182/j.issn1004731x.joss.23-0211

摘要/Abstract

摘要：

针对永磁直线同步电机(permanent magnet linear synchronous machine, PMLSM)有限元仿真模型的计算时间长，不能直观地显示结构参数与输出推力的关系，无法指导电机结构参数优化等问题，提出基于子域解析法和深度神经网络算法的PMLSM改进仿真模型，根据麦克斯韦方程组计算得到电机的磁通密度、空载反电势等性能数据，结合深度神经网络算法拟合出电机结构参数与输出推力的非线性关系。基于此模型，使用自适应遗传算法对PMLSM的推力密度进行优化，并与有限元仿真结果对比。结果表明：PMLSM改进仿真模型的计算速度是有限元模型的87.1倍，推力计算结果与有限元结果的平均误差为2.87%，优化后的电机推力密度提高了5.7%。

关键词: 永磁直线同步电机, 子域解析法, 深度神经网络, 自适应遗传算法, 推力优化

Abstract:

The finite element model (FEM) of permanent magnet linear synchronous machines (PMLSMs) takes a long computing time and cannot directly display the relationship between structural parameters and output thrust, thus failing to guide the structural parameter optimization of the machine. An improved simulation model of PMLSMs based on the subdomain analytical method and deep neural network (DNN) algorithm is proposed. The magnetic flux density, no-load counter electromotive force (EMF), and other data are obtained according to Maxwell's equations. The nonlinear relationship between the structural parameters of the machine and output thrust is fitted by the DNN algorithm. Based on this model, an adaptive genetic algorithm is used to optimize the thrust density of PMLSMs, and the results are compared with the finite element simulation. The results show that the computing speed of the improved simulation model of PMLSMs is 87.1 times that of the FEM. The average error of thrust between these two models is 2.87%, and the optimized thrust density of the machine is increased by 5.7%.

Key words: permanent magnet linear synchronous machine, subdomain analytical method, deep neural network, adaptive genetic algorithm, thrust optimization

中图分类号:

TM351

阎世梁,王银玲,路丹丹等 . 基于深度神经网络的永磁直线电机仿真与优化[J]. 系统仿真学报, 2024, 36(3): 713-725.

Yan Shiliang,Wang Yinling,Lu Dandan,et al . Simulation and Optimization of Permanent Magnet Linear Machine Based on Deep Neural Network[J]. Journal of System Simulation, 2024, 36(3): 713-725.

图/表 24

图1

表1

图2

图3

表2

图4

图5

图6

图7

图8

表3

PMLSM样本数据

序号	结构参数				推力性能
序号	τ_s/mm	h_s/mm	h_m/mm	α_p	$F ¯$ /N	$γ$ /%
1	11.4	40.0	4.5	0.74	285.2	19.3
2	11.6	40.0	4.5	0.74	290.6	18.9
3	11.8	40.0	4.5	0.74	296.7	18.6
$⋮$
20 734	13.6	42.2	5.6	0.83	424.5	13.6
20 735	13.6	42.2	5.6	0.84	429.8	13.4
20 736	13.6	42.2	5.6	0.85	435.6	13.1

表3

图9

表4

图10

图11

图12

表5

图13

图14

表6

图15

表7

优化后的电机参数与推力性能

结构参数	数值	推力性能	数值
τ_s/mm	12.1	$F ¯$ /N	371.7
h_s/mm	41.6	$γ$ /%	14.6
h_m/mm	5.2	推力密度/(N/mm³)	3.483
α_p	0.76	推力密度/(N/mm³)	3.483

表7

图16

图17

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结构参数	初始值	优化范围
极槽比	6槽5极	—
横向长度L/mm	55	—
次级铁芯高度h_c/mm	12	—
次级极距τ_p/mm	27	—
永磁体高度h_m/mm	5	4.5~5.6
极弧系数α_p	0.8	0.74~0.85
气隙长度g/mm	1.5	—
一个周期解析域长度τ_k/mm	270
初级齿槽宽度τ_s/mm	12.5	11.4~13.6
初级齿槽深度h_s/mm	41	40.0~42.2

子域编号	物理空间	子域编号	物理空间
①	顶端空气域	④	永磁体域
②	初级端部域	⑤	绕组线圈域
③	气隙域

名称	值
周期epoch	95
批大小batch-size	128
学习率α	1e-3
一阶矩指数衰减率β₁	0.9
一阶矩指数衰减率β₂	0.999
ε	1e-8
暂退概率drop rate	0.3、0.5、0.2

模型	R²
DNN	0.96~0.97
KNN	0.90~0.94

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