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Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

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posted on 08.12.2021, 23:03 by Yuki ShimizuYuki Shimizu, Shigeo Morimoto, Masayuki Sanada, Yukinori Inoue
The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

Funding

JST, ACT-X Grant Number JPMJAX20AE, Japan

History

Email Address of Submitting Author

de104004@edu.osakafu-u.ac.jp

ORCID of Submitting Author

0000-0001-8828-9563

Submitting Author's Institution

Osaka Prefecture University

Submitting Author's Country

Japan