Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network

Abstract

The heat loss and cooling modes of a permanent magnet synchronous motor (PMSM) directly affect the its temperature rise. The accurate evaluation and prediction of stator winding temperature is of great significance to the safety and reliability of PMSMs. In order to study the influencing factors of stator winding temperature and prevent motor insulation ageing, insulation burning, permanent magnet demagnetization and other faults caused by high stator winding temperature, we propose a computer model for PMSM temperature prediction. Ambient temperature, coolant temperature, direct-axis voltage, quadrature-axis voltage, motor speed, torque, direct-axis current, quadrature-axis current, permanent magnet surface temperature, stator yoke temperature, and stator tooth temperature are taken as the input, while the stator winding temperature is taken as the output. A deep neural network (DNN) model for PMSM temperature prediction was constructed. The experimental results showed the prediction error of the model (MAE) was 0.1515, the RMSE was 0.2368, the goodness of fit (R2) was 0.9439 and the goodness of fit between the predicted data and the measured data was high. Through comparative experiments, the prediction accuracy of the DNN model proposed in this paper was determined to be better than other models. This model can effectively predict the temperature change of stator winding, provide technical support to temperature early warning systems and ensure safe operation of PMSMs.