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Nondestructive characterization of solar PV cells defects by means of electroluminescence, infrared thermography, I–V curves and visual tests: Experimental study and comparison

Author

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  • 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-Rebollo, Miguel Ángel
  • Martínez-Sacristán, Oscar

Abstract

Photovoltaic (PV) modules are the core of every PV system, representing the power generation and their operation will affect the overall plant performance. It is one of the elements within a PV site with the higher failure appearance, being essential their proper operation to produce reliable, efficient and safety energy. Quantitative analysis and characterization of manufacturing, soldering and breaking PV defects is performed by a combination of electroluminescence (EL), infrared thermography (IRT), electrical current voltage (I–V) curves and visual inspection. Equivalent-circuit model characterization and microscope inspection are also performed as additional techniques when they contribute to the defects characterization. A 60-cells polycrystalline module has been ad hoc manufactured for this research, with different defective and non-defective cells. All cells are accessible from the backside of the module and the module includes similar kinds of defects in the same bypass string. This paper characterizes different defects of PV modules to control, mitigate or eliminate their influence and being able to do a quality assessment of a whole PV module, relating the individual cells performance with the combination of defective and non-defective cells within the module strings, with the objective of determining their interaction and mismatch effects, apart from their discrete performance.

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  • 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).
    • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220310379
    DOI: 10.1016/j.energy.2020.117930
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    Cited by:

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    3. Héctor Felipe Mateo Romero & Luis Hernández-Callejo & Miguel Ángel González Rebollo & Valentín Cardeñoso-Payo & Victor Alonso Gómez & Hugo Jose Bello & Ranganai Tawanda Moyo & Jose Ignacio Morales Ara, 2023. "Synthetic Dataset of Electroluminescence Images of Photovoltaic Cells by Deep Convolutional Generative Adversarial Networks," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    4. V S Bharath Kurukuru & Ahteshamul Haque & Arun Kumar Tripathy & Mohammed Ali Khan, 2022. "Machine learning framework for photovoltaic module defect detection with infrared images," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1771-1787, August.
    5. 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).
    6. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
    7. Zhao, Xiaolong & Song, Chonghui & Zhang, Haifeng & Sun, Xianrui & Zhao, Jing, 2023. "HRNet-based automatic identification of photovoltaic module defects using electroluminescence images," Energy, Elsevier, vol. 267(C).

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