The Temperature Prediction of Permanent Magnet Synchronous Machines Based on Proximal Policy Optimization

Yuefeng Cen,Chenguang Zhang,Cheng Zhao,Yulai Zhang,Gang Cen

doi:10.3390/info11110495

Yuefeng Cen, Chenguang Zhang + Show 3 more

Open Access

https://doi.org/10.3390/info11110495

Copy DOI

Journal: Information	Publication Date: Oct 23, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: Zhejiang University of Science and Technology

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).

Highlights

Temperature prediction of permanent magnet synchronous machines (PMSMs) has been a research focus in the field of motor protection
In order to provide an accurate prediction method, we propose a method based on correlation analysis (CA) and proximal policy optimization (PPO) [14]
The curves of long short-term memory (LSTM) and the recurrent neural network (RNN) conformed to the real target curves at first, they largely deviate at the end

Summary

Introduction

Temperature prediction of permanent magnet synchronous machines (PMSMs) has been a research focus in the field of motor protection. Researchers have made many attempts to predict the temperature of PMSMs [1], since temperature is an important factor for PMSMs to work. Most researchers have focused on the thermal model of the motor. The temperature equivalent model based on hardware-in-loop (HIL) was proposed to effectively predict the motor temperature [2], but this method required high calculation complexity. An equivalent thermal transfer model with two heat nodes for a permanent magnet synchronous motor was proposed [3]. Mohamed et al [4] constructed a Lumped Parameter Thermal Network (LPTN) to calculate important component temperatures inside PMSMs. The air temperature between permanent magnets was considered in this model.

Objectives

Methods

Results

Conclusion