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 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.