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Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part III

Research Article

Monitoring Method of Permanent Magnet Synchronous Motor Temperature Variation Signal Based on Model Prediction

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  • @INPROCEEDINGS{10.1007/978-3-031-50549-2_13,
        author={Li Liu and Jintian Yin and Dabing Sun and Hui Li and Qunfeng Zhu},
        title={Monitoring Method of Permanent Magnet Synchronous Motor Temperature Variation Signal Based on Model Prediction},
        proceedings={Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part III},
        proceedings_a={ADHIP PART 3},
        year={2024},
        month={3},
        keywords={Model prediction Permanent magnet synchronous motor Abnormal temperature signal Monitor Wireless sensor technology},
        doi={10.1007/978-3-031-50549-2_13}
    }
    
  • Li Liu
    Jintian Yin
    Dabing Sun
    Hui Li
    Qunfeng Zhu
    Year: 2024
    Monitoring Method of Permanent Magnet Synchronous Motor Temperature Variation Signal Based on Model Prediction
    ADHIP PART 3
    Springer
    DOI: 10.1007/978-3-031-50549-2_13
Li Liu1, Jintian Yin1,*, Dabing Sun2, Hui Li1, Qunfeng Zhu1
  • 1: Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University
  • 2: Hunan Jianwang Shengke New Energy Technology Co., Ltd.
*Contact email: yinjintian112@yeah.net

Abstract

In order to improve the monitoring accuracy and quality of permanent magnet synchronous motor (PMSM) temperature variation signal, and achieve the ideal effect of high-precision monitoring of PMSM temperature variation signal, model prediction is introduced, and the monitoring method of PMSM temperature variation signal based on model prediction is studied. The wireless sensor technology is used to collect the temperature signals of various parts of the motor, integrate, clean, replace and protocol the original data, establish a deep learning network model to predict the characteristics of the motor temperature variation, identify the motor temperature variation signal, combine the variation pruning and variation interval, and use the delayed reporting strategy to monitor the early warning of the motor temperature variation signal, complete the monitoring of temperature variation signal of permanent magnet synchronous motor based on model prediction. The experimental analysis results show that the recall rate and accuracy rate of the design method are above 90%, and maintain detection efficiency above 97%, the monitoring accuracy of the temperature variation signal of the permanent magnet synchronous motor is high.

Keywords
Model prediction Permanent magnet synchronous motor Abnormal temperature signal Monitor Wireless sensor technology
Published
2024-03-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-50549-2_13
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