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Probability modeling for PV array output interval and its application in fault diagnosis

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  • Wang, Haizheng
  • Zhao, Jian
  • Sun, Qian
  • Zhu, Honglu

Abstract

It is crucial to detect fault arrays timely to ensure the safe and economic operation of large-scale photovoltaic (PV) power plants. The inconsistencies among different arrays and the random fluctuations of PV output result in uncertainties of the PV array output, and the deterministic fault diagnosis methods decrease the accuracy of PV fault diagnosis methods. In this paper, a probability modeling approach for a PV array electrical parameter distribution, which can effectively solve the problems of the nonlinearity and uncertainty of the PV array output interval, is proposed. Actual PV plant data are utilized to calculate fault indicators and analyze the uncertainty of fault indicator distributions. The t-location scale distribution function is used to fit the fault indicator distributions and to establish a probability model of PV fault indicators in different irradiance ranges. Finally, fault indicator thresholds with different degrees of confidence are obtained to complete the PV array fault diagnosis. The setting of the indicator threshold for PV arrays is based on measured data rather than experience, and it can effectively detect different types of faults. The effectiveness of the proposed method is verified in a real PV plant.

Suggested Citation

  • Wang, Haizheng & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2019. "Probability modeling for PV array output interval and its application in fault diagnosis," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319437
    DOI: 10.1016/j.energy.2019.116248
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    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
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    Cited by:

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    3. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
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    5. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
    6. Jingwei Zhang & Zenan Yang & Kun Ding & Li Feng & Frank Hamelmann & Xihui Chen & Yongjie Liu & Ling Chen, 2022. "Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics," Energies, MDPI, vol. 15(18), pages 1-17, September.
    7. Sun, Chenhao & Zhou, Zhuoyu & Zeng, Xiangjun & Li, Zewen & Wang, Yuanyuan & Deng, Feng, 2022. "A multi-model-integration-based prediction methodology for the spatiotemporal distribution of vulnerabilities in integrated energy systems under the multi-type, imbalanced, and dependent input data sc," Applied Energy, Elsevier, vol. 320(C).
    8. Hocine, Labar & Samira, Kelaiaia Mounia & Tarek, Mesbah & Salah, Necaibia & Samia, Kelaiaia, 2021. "Automatic detection of faults in a photovoltaic power plant based on the observation of degradation indicators," Renewable Energy, Elsevier, vol. 164(C), pages 603-617.

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