IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i4p1629-d492675.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/4/1629/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/4/1629/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
    2. 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.
    3. 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.
    4. Li, Yuanliang & Ding, Kun & Zhang, Jingwei & Chen, Fudong & Chen, Xiang & Wu, Jiabing, 2019. "A fault diagnosis method for photovoltaic arrays based on fault parameters identification," Renewable Energy, Elsevier, vol. 143(C), pages 52-63.
    5. 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.
    6. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    7. Dhimish, Mahmoud & Holmes, Violeta & Dales, Mark, 2017. "Parallel fault detection algorithm for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 113(C), pages 94-111.
    8. Osmani, Khaled & Haddad, Ahmad & Lemenand, Thierry & Castanier, Bruno & Ramadan, Mohamad, 2021. "An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters," Energy, Elsevier, vol. 224(C).
    9. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
    10. 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.
    11. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    12. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    13. Pillai, Dhanup S. & Rajasekar, N., 2018. "Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3503-3525.
    14. Mellit, Adel & Kalogirou, Soteris, 2022. "Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems," Renewable Energy, Elsevier, vol. 184(C), pages 1074-1090.
    15. Bressan, M. & Gutierrez, A. & Garcia Gutierrez, L. & Alonso, C., 2018. "Development of a real-time hot-spot prevention using an emulator of partially shaded PV systems," Renewable Energy, Elsevier, vol. 127(C), pages 334-343.
    16. Das, Saborni & Hazra, Abhik & Basu, Mousumi, 2018. "Metaheuristic optimization based fault diagnosis strategy for solar photovoltaic systems under non-uniform irradiance," Renewable Energy, Elsevier, vol. 118(C), pages 452-467.
    17. 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.
    18. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2023. "Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks," Energy, Elsevier, vol. 266(C).
    19. 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.
    20. Ramadoss Janarthanan & R. Uma Maheshwari & Prashant Kumar Shukla & Piyush Kumar Shukla & Seyedali Mirjalili & Manoj Kumar, 2021. "Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems," Energies, MDPI, vol. 14(20), pages 1-19, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1629-:d:492675. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.