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Fault detection method for grid-connected photovoltaic plants

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  • Chine, W.
  • Mellit, A.
  • Pavan, A. Massi
  • Kalogirou, S.A.

Abstract

In this work, an automatic fault detection method for grid-connected photovoltaic (GCPV) plants is presented. The proposed method generates a diagnostic signal which indicates possible faults occurring in the GCPV plant. In order to determine the location of the fault, the ratio between DC and AC power is monitored. The software tool developed identifies different types of faults like: fault in a photovoltaic module, fault in a photovoltaic string, fault in an inverter, and a general fault that may include partial shading, PV ageing, or MPPT error. In addition to the diagnostic signal, other essential information about the system can be displayed each 10 min on the designed tool. The method has been validated using an experimental database of climatic and electrical parameters regarding a 20 kWp GCPV plant installed on the rooftop of the municipality of Trieste, Italy. The obtained results indicate that the proposed method can detect and locate correctly different type of faults in both DC and AC sides of the GCPV plant. The developed software can help users to check possible faults on their systems in real time.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:66:y:2014:i:c:p:99-110
    DOI: 10.1016/j.renene.2013.11.073
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Triki-Lahiani, Asma & Bennani-Ben Abdelghani, Afef & Slama-Belkhodja, Ilhem, 2018. "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2680-2692.
    5. 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.
    6. Jun-Hyun Shin & Jin-O Kim, 2020. "On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant," Energies, MDPI, vol. 13(17), pages 1-12, September.
    7. 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.
    8. 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.
    9. 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.
    10. Gallardo-Saavedra, Sara & Hernández-Callejo, Luis & Alonso-García, María del Carmen & Santos, José Domingo & Morales-Aragonés, José Ignacio & Alonso-Gómez, Víctor & Moretón-Fernández, Ángel & González, 2020. "Nondestructive characterization of solar PV cells defects by means of electroluminescence, infrared thermography, I–V curves and visual tests: Experimental study and comparison," Energy, Elsevier, vol. 205(C).
    11. Carlos Toledo & Lucía Serrano-Lujan & Jose Abad & Antonio Lampitelli & Antonio Urbina, 2019. "Measurement of Thermal and Electrical Parameters in Photovoltaic Systems for Predictive and Cross-Correlated Monitorization," Energies, MDPI, vol. 12(4), pages 1-20, February.
    12. 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.
    13. 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.
    14. Isabel Santiago & David Trillo Montero & Juan J. Luna Rodríguez & Isabel M. Moreno Garcia & Emilio J. Palacios Garcia, 2017. "Graphical Diagnosis of Performances in Photovoltaic Systems: A Case Study in Southern Spain," Energies, MDPI, vol. 10(12), pages 1-26, November.
    15. Odysseas Tsafarakis & Kostas Sinapis & Wilfried G. J. H. M. van Sark, 2019. "A Time-Series Data Analysis Methodology for Effective Monitoring of Partially Shaded Photovoltaic Systems," Energies, MDPI, vol. 12(9), pages 1-18, May.
    16. 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.
    17. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan, 2020. "An Online Novel Two-Layered Photovoltaic Fault Monitoring Technique Based Upon the Thermal Signatures," Sustainability, MDPI, vol. 12(22), pages 1-13, November.
    18. 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.
    19. Kara Mostefa Khelil, Chérifa & Amrouche, Badia & Benyoucef, Abou soufiane & Kara, Kamel & Chouder, Aissa, 2020. "New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems," Energy, Elsevier, vol. 211(C).
    20. 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.
    21. 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.
    22. Lin, Wenye & Ma, Zhenjun & Li, Kehua & Tyagi, V.V. & Pandey, A.K., 2021. "A dynamic simulation platform for fault modelling and characterisation of building integrated photovoltaics," Renewable Energy, Elsevier, vol. 179(C), pages 963-981.
    23. 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.
    24. Kichou, Sofiane & Silvestre, Santiago & Nofuentes, Gustavo & Torres-Ramírez, Miguel & Chouder, Aissa & Guasch, Daniel, 2016. "Characterization of degradation and evaluation of model parameters of amorphous silicon photovoltaic modules under outdoor long term exposure," Energy, Elsevier, vol. 96(C), pages 231-241.
    25. 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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