Torque and Temperature Prediction for Permanent Magnet Synchronous Motor Using Neural Networks

Abstract

This paper focuses on developing a torque and stator temperature prediction model for permanent magnet synchronous motors using neural networks. The model can predict torque and four other temperature parameters at the permanent magnet surface, stator's yoke, tooth, and winding. The motor's torque and temperatures are predicted without installing any additional sensors into it. Using the training dataset with Levenberg-Marquardt optimization and Bayesian regularization algorithms, the predicted model has the best performance with the least mean square error and the best R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values. Also, the prediction of testing data shows that the estimated model follows closely with actual values. This is true for all the five output parameters.