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