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

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 prediction model that can replace FEA. However, the training data are generated by FEA, it takes very obtain sufficient amount data, making impossible train large-scale model. Here, we propose method for generating large from small number FEA results using machine learning. An automatic system with deep generative and convolutional neural network then constructed. With its proposed handle three topologies motor parameters in wide range current vector regions. was applied multi-objective optimization design, completed 13–15 seconds.