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Fault detection and diagnosis methods for photovoltaic systems: A review

Author

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  • Mellit, A.
  • Tina, G.M.
  • Kalogirou, S.A.

Abstract

Faults in any components (modules, connection lines, converters, inverters, etc.) of photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) can seriously affect the efficiency, energy yield as well as the security and reliability of the entire PV plant, if not detected and corrected quickly. In addition, if some faults persist (e.g. arc fault, ground fault and line-to-line fault) they can lead to risk of fire. Fault detection and diagnosis (FDD) methods are indispensable for the system reliability, operation at high efficiency, and safety of the PV plant. In this paper, the types and causes of PV systems (PVS) failures are presented, then different methods proposed in literature for FDD of PVS are reviewed and discussed; particularly faults occurring in PV arrays (PVA). Special attention is paid to methods that can accurately detect, localise and classify possible faults occurring in a PVA. The advantages and limits of FDD methods in terms of feasibility, complexity, cost-effectiveness and generalisation capability for large-scale integration are highlighted. Based on the reviewed papers, challenges and recommendations for future research direction are also provided.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:rensus:v:91:y:2018:i:c:p:1-17
    DOI: 10.1016/j.rser.2018.03.062
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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. Massi Pavan, A. & Mellit, A. & De Pieri, D. & Kalogirou, S.A., 2013. "A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants," Applied Energy, Elsevier, vol. 108(C), pages 392-401.
    3. 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.
    4. 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.
    5. Tsanakas, John A. & Ha, Long & Buerhop, Claudia, 2016. "Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 695-709.
    6. Hernández, J.C. & Vidal, P.G. & Medina, A., 2010. "Characterization of the insulation and leakage currents of PV generators: Relevance for human safety," Renewable Energy, Elsevier, vol. 35(3), pages 593-601.
    7. Adinoyi, Muhammed J. & Said, Syed A.M., 2013. "Effect of dust accumulation on the power outputs of solar photovoltaic modules," Renewable Energy, Elsevier, vol. 60(C), pages 633-636.
    8. 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.
    9. Kalogirou, Soteris A. & Agathokleous, Rafaela & Panayiotou, Gregoris, 2013. "On-site PV characterization and the effect of soiling on their performance," Energy, Elsevier, vol. 51(C), pages 439-446.
    10. Colli, Alessandra, 2015. "Failure mode and effect analysis for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 804-809.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

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