{"title":"Rotor Temperature Prediction of PMSM Based on LSTM Neural Networks","authors":"Liange He, Yuhang Feng, Zhang Yan, Meijing Cai","doi":"10.1007/s13369-024-09213-0","DOIUrl":null,"url":null,"abstract":"<div><p>The rotor of the permanent magnet synchronous motor develops localized high temperatures at high-torque or high-speed operating conditions so that the demagnetization failure phenomenon may occur. To address this problem, a rotor temperature prediction model based on long-and-short-term memory (LSTM) neural networks is proposed. In addition, the effects of several hyperparameters on the network construction are investigated. To better improve the accuracy of prediction results, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used to optimize the construction of the network parameters. The results of the study show that the LSTM model has a large error throughout the process, which ranges from − 2.66–6.64 °C. GA-LSTM has an error of − 1.71 ~ 3.91 ℃ throughout the process. The error of PSO-LSTM is − 1.78 ~ 0.96 ℃. Additionally, the proposed PSO-LSTM prediction model exhibits good accuracy and stability with RMSE of 0.7114 and MAPE of 1.22%.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"49 12","pages":"16685 - 16696"},"PeriodicalIF":2.6000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Arabian Journal for Science and Engineering","FirstCategoryId":"103","ListUrlMain":"https://link.springer.com/article/10.1007/s13369-024-09213-0","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The rotor of the permanent magnet synchronous motor develops localized high temperatures at high-torque or high-speed operating conditions so that the demagnetization failure phenomenon may occur. To address this problem, a rotor temperature prediction model based on long-and-short-term memory (LSTM) neural networks is proposed. In addition, the effects of several hyperparameters on the network construction are investigated. To better improve the accuracy of prediction results, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used to optimize the construction of the network parameters. The results of the study show that the LSTM model has a large error throughout the process, which ranges from − 2.66–6.64 °C. GA-LSTM has an error of − 1.71 ~ 3.91 ℃ throughout the process. The error of PSO-LSTM is − 1.78 ~ 0.96 ℃. Additionally, the proposed PSO-LSTM prediction model exhibits good accuracy and stability with RMSE of 0.7114 and MAPE of 1.22%.
期刊介绍:
King Fahd University of Petroleum & Minerals (KFUPM) partnered with Springer to publish the Arabian Journal for Science and Engineering (AJSE).
AJSE, which has been published by KFUPM since 1975, is a recognized national, regional and international journal that provides a great opportunity for the dissemination of research advances from the Kingdom of Saudi Arabia, MENA and the world.