loading page

Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor
  • +1
  • Yuki Shimizu ,
  • Shigeo Morimoto ,
  • Masayuki Sanada ,
  • Yukinori Inoue
Yuki Shimizu
Author Profile
Shigeo Morimoto
Author Profile
Masayuki Sanada
Author Profile
Yukinori Inoue
Author Profile


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.
Mar 2023Published in IEEE Transactions on Energy Conversion volume 38 issue 1 on pages 724-734. 10.1109/TEC.2022.3208129