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Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review

Author

Listed:
  • Qamar Navid

    (Emirates Centre for Energy & Environment Research, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

  • Ahmed Hassan

    (Department of Architecture Engineering, College of Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

  • Abbas Ahmad Fardoun

    (Department of Electrical and Electronic Engineering, Al Mareef University, Beirut 1001, Lebanon)

  • Rashad Ramzan

    (Department of Electrical Engineering, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan)

  • Abdulrahman Alraeesi

    (Department of Chemical and Petroleum Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

Abstract

The worldwide electricity supply network has recently experienced a huge rate of solar photovoltaic penetration. Grid-connected photovoltaic (PV) systems range from smaller custom built-in arrays to larger utility power plants. When the size and share of PV systems in the energy mix increases, the operational complexity and reliability of grid stability also increase. The growing concern about PV plants compared to traditional power plants is the dispersed existence of PV plants with millions of generators (PV panels) spread over kilometers, which increases the possibility of faults occurring and associated risk. As a result, a robust fault diagnosis and mitigation framework remain a key component of PV plants. Various fault monitoring and diagnostic systems are currently being used, defined by calculation of electrical parameters, extracted electrical parameters, artificial intelligence, and thermography. This article explores existing PV fault diagnostic systems in a detailed way and addresses their possible merits and demerits.

Suggested Citation

  • Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan & Abdulrahman Alraeesi, 2021. "Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1629-:d:492675
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    References listed on IDEAS

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    1. Tsanakas, John A. & Ha, Long D. & Al Shakarchi, F., 2017. "Advanced inspection of photovoltaic installations by aerial triangulation and terrestrial georeferencing of thermal/visual imagery," Renewable Energy, Elsevier, vol. 102(PA), pages 224-233.
    2. Ramli, Makbul A.M. & Twaha, Ssennoga & Ishaque, Kashif & Al-Turki, Yusuf A., 2017. "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 144-159.
    3. Silvestre, Santiago & Kichou, Sofiane & Chouder, Aissa & Nofuentes, Gustavo & Karatepe, Engin, 2015. "Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions," Energy, Elsevier, vol. 86(C), pages 42-50.
    4. Silvestre, S. & Boronat, A. & Chouder, A., 2009. "Study of bypass diodes configuration on PV modules," Applied Energy, Elsevier, vol. 86(9), pages 1632-1640, September.
    5. Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Chong, Benjamin & Zhang, Li, 2017. "Seven indicators variations for multiple PV array configurations under partial shading and faulty PV conditions," Renewable Energy, Elsevier, vol. 113(C), pages 438-460.
    6. Dolara, Alberto & Lazaroiu, George Cristian & Leva, Sonia & Manzolini, Giampaolo, 2013. "Experimental investigation of partial shading scenarios on PV (photovoltaic) modules," Energy, Elsevier, vol. 55(C), pages 466-475.
    7. Mellit, Adel & Kalogirou, Soteris A., 2011. "ANFIS-based modelling for photovoltaic power supply system: A case study," Renewable Energy, Elsevier, vol. 36(1), pages 250-258.
    8. Youssef, Ayman & El-Telbany, Mohammed & Zekry, Abdelhalim, 2017. "The role of artificial intelligence in photo-voltaic systems design and control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 72-79.
    9. Madeti, Siva Ramakrishna & Singh, S.N., 2017. "Online fault detection and the economic analysis of grid-connected photovoltaic systems," Energy, Elsevier, vol. 134(C), pages 121-135.
    10. Celik, Berk & Karatepe, Engin & Gokmen, Nuri & Silvestre, Santiago, 2013. "A virtual reality study of surrounding obstacles on BIPV systems for estimation of long-term performance of partially shaded PV arrays," Renewable Energy, Elsevier, vol. 60(C), pages 402-414.
    11. Celik, Berk & Karatepe, Engin & Silvestre, Santiago & Gokmen, Nuri & Chouder, Aissa, 2015. "Analysis of spatial fixed PV arrays configurations to maximize energy harvesting in BIPV applications," Renewable Energy, Elsevier, vol. 75(C), pages 534-540.
    12. Djordjevic, Sinisa & Parlevliet, David & Jennings, Philip, 2014. "Detectable faults on recently installed solar modules in Western Australia," Renewable Energy, Elsevier, vol. 67(C), pages 215-221.
    13. Omer, Abdeen Mustafa, 2007. "Renewable energy resources for electricity generation in Sudan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(7), pages 1481-1497, September.
    14. Parida, Bhubaneswari & Iniyan, S. & Goic, Ranko, 2011. "A review of solar photovoltaic technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1625-1636, April.
    15. Qi, Jun & Zhang, Youbing & Chen, Yi, 2014. "Modeling and maximum power point tracking (MPPT) method for PV array under partial shade conditions," Renewable Energy, Elsevier, vol. 66(C), pages 337-345.
    16. Lin, Faa-Jeng & Lu, Kuang-Chin & Ke, Ting-Han, 2016. "Probabilistic Wavelet Fuzzy Neural Network based reactive power control for grid-connected three-phase PV system during grid faults," Renewable Energy, Elsevier, vol. 92(C), pages 437-449.
    17. Chine, W. & Mellit, A. & Pavan, A. Massi & Kalogirou, S.A., 2014. "Fault detection method for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 66(C), pages 99-110.
    18. 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.
    19. Jung, Tae Hee & Lee, Jeong In & Song, Hee-eun & Ju, Young Chul & Ko, Suk Whan & Jung, Young-Seok & Kang, Gi Hwan, 2017. "Classification conditions of cells to reduce cell-to-module conversion loss at the production stage of PV modules," Renewable Energy, Elsevier, vol. 103(C), pages 582-593.
    20. Bressan, M. & El Basri, Y. & Galeano, A.G. & Alonso, C., 2016. "A shadow fault detection method based on the standard error analysis of I-V curves," Renewable Energy, Elsevier, vol. 99(C), pages 1181-1190.
    21. Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
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    2. Buratti, Yoann & Javier, Gaia M.N. & Abdullah-Vetter, Zubair & Dwivedi, Priya & Hameiri, Ziv, 2024. "Machine learning for advanced characterisation of silicon photovoltaics: A comprehensive review of techniques and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).

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