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Evaluation Model of Operation State Based on Deep Learning for Smart Meter

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  • Qingsheng Zhao

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China)

  • Juwen Mu

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China)

  • Xiaoqing Han

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China)

  • Dingkang Liang

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China)

  • Xuping Wang

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

Suggested Citation

  • Qingsheng Zhao & Juwen Mu & Xiaoqing Han & Dingkang Liang & Xuping Wang, 2021. "Evaluation Model of Operation State Based on Deep Learning for Smart Meter," Energies, MDPI, vol. 14(15), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4674-:d:606682
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    References listed on IDEAS

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