The temperature prediction of permanent magnet synchronous machines based on proximal policy optimization

Abstract

Accurate temperature prediction plays an important role in the thermal protection of permanent magnet synchronous motors. A temperature prediction method of permanent magnet synchronous machines (PMSMs) based on proximal policy optimization is proposed. In the proposed method, the actor-critic framework of reinforcement learning is introduced to model the effective temperature prediction mechanism, and the correlations between the input features are then analyzed to select the appropriate input features. Finally, the simplified proximal policy optimization algorithm is introduced to optimize the value of the prediction temperature of PMSMs. Experimental results reveal the high accuracy and reliability of the proposed method compared with an exponential weighted moving average method (EWMA), a recurrent neural network (RNN), and long short-term memory (LSTM).