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A shadow fault detection method based on the standard error analysis of I-V curves

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  • Bressan, M.
  • El Basri, Y.
  • Galeano, A.G.
  • Alonso, C.

Abstract

Shading on photovoltaic (PV) modules induces disproportionate impacts on power production. This paper presents a fault detection method able to identify anomalies on PV systems such as shading problems. The presence of localized shading on PV modules leads to an overheating of the shaded PV cells despite the activation of by-pass diodes. The temperature increase reduces considerably PV module performances and its lifetime. The presented method uses a simple equation, which corresponds to the normalized error (DE) of the comparison between the I-V curve in normal operation and the I-V curve in shading condition. The first derivative calculation gives the area of the detection in function of the PV voltage of the module (DE/DV). This defines whether one or several PV cells dissipate power. This phenomenon essentially occurs in the case of non-uniform irradiance received on PV modules and could impact PV modules performances. The detection method is explained in detail through the study of specific shadows simulations on PV modules. The results are validated through experimental tests on PV modules.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:1181-1190
    DOI: 10.1016/j.renene.2016.08.028
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    References listed on IDEAS

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    5. Orozco-Gutierrez, M.L. & Ramirez-Scarpetta, J.M. & Spagnuolo, G. & Ramos-Paja, C.A., 2014. "A method for simulating large PV arrays that include reverse biased cells," Applied Energy, Elsevier, vol. 123(C), pages 157-167.
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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. 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.
    3. Rico Espinosa, Alejandro & Bressan, Michael & Giraldo, Luis Felipe, 2020. "Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 249-256.
    4. Qu, Jiaqi & Sun, Qiang & Qian, Zheng & Wei, Lu & Zareipour, Hamidreza, 2024. "Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules," Applied Energy, Elsevier, vol. 355(C).
    5. Mariana Durango-Flórez & Daniel González-Montoya & Luz Adriana Trejos-Grisales & Carlos Andres Ramos-Paja, 2022. "PV Array Reconfiguration Based on Genetic Algorithm for Maximum Power Extraction and Energy Impact Analysis," Sustainability, MDPI, vol. 14(7), pages 1-14, March.
    6. 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.
    7. 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.
    8. Yadav, Anurag Singh & Mukherjee, V., 2021. "Conventional and advanced PV array configurations to extract maximum power under partial shading conditions: A review," Renewable Energy, Elsevier, vol. 178(C), pages 977-1005.
    9. 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.
    10. Alonso Gutiérrez Galeano & Michael Bressan & Fernando Jiménez Vargas & Corinne Alonso, 2018. "Shading Ratio Impact on Photovoltaic Modules and Correlation with Shading Patterns," Energies, MDPI, vol. 11(4), pages 1-26, April.
    11. Rajput, Pramod & Shyam, & Tomar, Vivek & Tiwari, G.N. & Sastry, O.S. & Bhatti, T.S., 2018. "A thermal model for N series connected glass/cell/polymer sheet and glass/cell/glass crystalline silicon photovoltaic modules with hot solar cells connected in series and its thermal losses in real ou," Renewable Energy, Elsevier, vol. 126(C), pages 370-386.
    12. 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.
    13. 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.

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