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