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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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    References listed on IDEAS

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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.
    2. Harrou, Fouzi & Sun, Ying & Taghezouit, Bilal & Saidi, Ahmed & Hamlati, Mohamed-Elkarim, 2018. "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, Elsevier, vol. 116(PA), pages 22-37.
    3. Mahela, Om Prakash & Shaik, Abdul Gafoor, 2017. "Comprehensive overview of grid interfaced solar photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 316-332.
    4. Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Mather, Peter, 2017. "Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system," Energy, Elsevier, vol. 140(P1), pages 276-290.
    5. Qiang Zhao & Shuai Shao & Lingxing Lu & Xin Liu & Honglu Zhu, 2018. "A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm," Energies, MDPI, vol. 11(1), pages 1-21, January.
    6. Pillai, Dhanup S. & Rajasekar, N., 2018. "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 18-40.
    7. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
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    Cited by:

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    7. Naveen Venkatesh Sridharan & Jerome Vasanth Joseph & Sugumaran Vaithiyanathan & Mohammadreza Aghaei, 2023. "Weightless Neural Network-Based Detection and Diagnosis of Visual Faults in Photovoltaic Modules," Energies, MDPI, vol. 16(15), pages 1-17, August.
    8. 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).
    9. 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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