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

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  • Hai Guo

    (Post-Doctoral Workstation of Electronic Engineering, Heilongjiang University, Harbin 150080, China
    College of Computer Science and Technology, Dalian Minzu University, Dalian 116650, China)

  • Qun Ding

    (Post-Doctoral Workstation of Electronic Engineering, Heilongjiang University, Harbin 150080, China)

  • Yifan Song

    (College of Computer Science and Technology, Dalian Minzu University, Dalian 116650, China)

  • Haoran Tang

    (College of Computer Science and Technology, Dalian Minzu University, Dalian 116650, China)

  • Likun Wang

    (College of Electronic and Electrical Engineering, Harbin University of Science and Technology, Harbin 150080, China)

  • Jingying Zhao

    (College of Computer Science and Technology, Dalian Minzu University, Dalian 116650, China
    Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116650, China)

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 ( R 2 ) 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.

Suggested Citation

  • Hai Guo & Qun Ding & Yifan Song & Haoran Tang & Likun Wang & Jingying Zhao, 2020. "Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network," Energies, MDPI, vol. 13(18), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4782-:d:413130
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    References listed on IDEAS

    as
    1. Jafari, Mostafa & Taher, Seyed Abbas, 2017. "Thermal survey of core losses in permanent magnet micro-motor," Energy, Elsevier, vol. 123(C), pages 579-584.
    2. Chen, Shi-An & Jiang, Xu-Dong & Yao, Ming & Jiang, Shun-Ming & Chen, Jinzhou & Wang, Ya-Xiong, 2020. "A dual vibration reduction structure-based self-powered active suspension system with PMSM-ball screw actuator via an improved H2/H∞ control," Energy, Elsevier, vol. 201(C).
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    Cited by:

    1. Faiz Husnayain & Toshihiko Noguchi & Ryosuke Akaki & Feri Yusivar, 2023. "Improved Current and MTPA Control Characteristics Using FEM-Based Inductance Maps for Vector-Controlled IPM Motor," Energies, MDPI, vol. 16(12), pages 1-22, June.
    2. Edison Gundabattini & Arkadiusz Mystkowski & Adam Idzkowski & Raja Singh R. & Darius Gnanaraj Solomon, 2021. "Thermal Mapping of a High-Speed Electric Motor Used for Traction Applications and Analysis of Various Cooling Methods—A Review," Energies, MDPI, vol. 14(5), pages 1-32, March.
    3. Junci Cao & Hua Yan & Dong Li & Yu Wang & Weili Li, 2021. "Influence of the Variable Cross-Section Stator Ventilation Structure on the Temperature of an Induction Motor," Energies, MDPI, vol. 14(17), pages 1-17, August.
    4. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.

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