Rotor Temperature Prediction of PMSM Based on LSTM Neural Networks

IF 2.6 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Arabian Journal for Science and Engineering Pub Date : 2024-06-08 DOI:10.1007/s13369-024-09213-0
Liange He, Yuhang Feng, Zhang Yan, Meijing Cai
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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%.

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基于 LSTM 神经网络的 PMSM 转子温度预测
永磁同步电机转子在高扭矩或高速运行条件下会产生局部高温,从而可能出现退磁故障现象。针对这一问题,提出了一种基于长短期记忆(LSTM)神经网络的转子温度预测模型。此外,还研究了多个超参数对网络构建的影响。为了更好地提高预测结果的准确性,采用了粒子群优化(PSO)和遗传算法(GA)来优化网络参数的构建。研究结果表明,LSTM 模型在整个过程中误差较大,误差范围为 - 2.66-6.64 ℃。GA-LSTM 在整个过程中的误差为 - 1.71 ~ 3.91 ℃。PSO-LSTM 的误差为 - 1.78 ~ 0.96 ℃。此外,所提出的 PSO-LSTM 预测模型具有良好的准确性和稳定性,RMSE 为 0.7114,MAPE 为 1.22%。
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来源期刊
Arabian Journal for Science and Engineering
Arabian Journal for Science and Engineering MULTIDISCIPLINARY SCIENCES-
CiteScore
5.70
自引率
3.40%
发文量
993
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.
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