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Journal of System Simulation

Abstract

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%.

First Page

713

Last Page

725

CLC

TM351; TP391.9

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.23-0211

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