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A Photovoltaic Fault Diagnosis Method Integrating Photovoltaic Power Prediction and EWMA Control Chart

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  • Jun Su

    (School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China
    Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen 361024, China)

  • Zhiyuan Zeng

    (School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China)

  • Chaolong Tang

    (School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China)

  • Zhiquan Liu

    (School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China)

  • Tianyou Li

    (School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China
    Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen 361024, China)

Abstract

The inevitability of faults arises due to prolonged exposure of photovoltaic (PV) power plants to intricate environmental conditions. Therefore, fault diagnosis of PV power plants is crucial to ensure the continuity and reliability of power generation. This paper proposes a fault diagnosis method that integrates PV power prediction and an exponentially weighted moving average (EWMA) control chart. This method predicts the PV power based on meteorological factors using the adaptive particle swarm algorithm-back propagation neural network (APSO-BPNN) model and takes its error from the actual value as a control quantity for the EWMA control chart. The EWMA control chart then monitors the error values to identify fault types. Finally, it is verified by comparison with the discrete rate (DR) analysis method. The results showed that the coefficient of determination of the prediction model of the proposed method reached 0.98. Although the DR analysis can evaluate the overall performance of the inverter and identify the faults, it often fails to point out the specific location of the faults accurately. In contrast, the EWMA control chart can monitor abnormal states such as open and short circuits and accurately locate the string where the fault occurs.

Suggested Citation

  • Jun Su & Zhiyuan Zeng & Chaolong Tang & Zhiquan Liu & Tianyou Li, 2024. "A Photovoltaic Fault Diagnosis Method Integrating Photovoltaic Power Prediction and EWMA Control Chart," Energies, MDPI, vol. 17(17), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4263-:d:1464355
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    References listed on IDEAS

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    1. Jun-Hyun Shin & Jin-O Kim, 2020. "On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant," Energies, MDPI, vol. 13(17), pages 1-12, September.
    2. Ding, Kun & Chen, Xiang & Jiang, Meng & Yang, Hang & Chen, Xihui & Zhang, Jingwei & Gao, Ruiguang & Cui, Liu, 2024. "Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion," Applied Energy, Elsevier, vol. 353(PB).
    3. Zixia Yuan & Guojiang Xiong & Xiaofan Fu, 2022. "Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey," Energies, MDPI, vol. 15(22), pages 1-18, November.
    4. Weihua Tian & Yan Bao & Wei Liu & Naeem Jan, 2022. "Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, April.
    5. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
    6. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    7. Huang, Songtao & Zhou, Qingguo & Shen, Jun & Zhou, Heng & Yong, Binbin, 2024. "Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting," Energy, Elsevier, vol. 290(C).
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