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Broad-scale Electroluminescence analysis of 5 million+ photovoltaic cells for defect detection and degradation assessment

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  • Hassan, Sharmarke
  • Dhimish, Mahmoud

Abstract

This paper presents a comprehensive study on the detection, classification, and impact of defects in photovoltaic (PV) modules, using Electroluminescence (EL) imaging as the primary diagnostic tool. The study inspects 85,000 PV modules across 167 installations over a nine-year period, with defects categorized into line cracks, complex cracks, edge-ribbon cracks, and Potential-Induced Degradation (PID). A key finding reveals an increasing prevalence of complex and edge-ribbon cracks in recent installations, highlighting potential shifts in manufacturing or installation processes. Despite these observed defects, the degradation rates of the PV systems remain below 1 % per year, with long-term projections indicating that most systems will retain over 80 % of their original capacity after 25 years. This study underscores the importance of early defect detection for maintaining system efficiency and longevity, and its findings are crucial for refining manufacturing and installation standards. Novel insights include the integration of EL imaging for defect classification and the suggestion of semantic segmentation techniques to further improve automated defect detection. These advancements hold the potential to optimize the predictive maintenance of PV systems, ensuring enhanced performance and reduced operational losses over time.

Suggested Citation

  • Hassan, Sharmarke & Dhimish, Mahmoud, 2024. "Broad-scale Electroluminescence analysis of 5 million+ photovoltaic cells for defect detection and degradation assessment," Renewable Energy, Elsevier, vol. 237(PD).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pd:s0960148124019360
    DOI: 10.1016/j.renene.2024.121868
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    References listed on IDEAS

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    1. Koo Lee & Sung Bae Cho & Junsin Yi & Hyo Sik Chang, 2022. "Simplified Recovery Process for Resistive Solder Bond (RSB) Hotspots Caused by Poor Soldering of Crystalline Silicon Photovoltaic Modules Using Resin," Energies, MDPI, vol. 15(13), pages 1-19, June.
    2. Hassan, Sharmarke & Dhimish, Mahmoud, 2023. "Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection," Renewable Energy, Elsevier, vol. 219(P1).
    3. Oyeniyi A. Alimi & Edson L. Meyer & Olufemi I. Olayiwola, 2022. "Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review," Energies, MDPI, vol. 15(16), pages 1-28, August.
    4. Aghaei, M. & Fairbrother, A. & Gok, A. & Ahmad, S. & Kazim, S. & Lobato, K. & Oreski, G. & Reinders, A. & Schmitz, J. & Theelen, M. & Yilmaz, P. & Kettle, J., 2022. "Review of degradation and failure phenomena in photovoltaic modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Magadley, Esther & Kabha, Ragheb & Dakka, Mohamad & Teitel, Meir & Friman-Peretz, Maayan & Kacira, Murat & Waller, Rebekah & Yehia, Ibrahim, 2022. "Organic photovoltaic modules integrated inside and outside a polytunnel roof," Renewable Energy, Elsevier, vol. 182(C), pages 163-171.
    6. Dhimish, Mahmoud & Ahmad, Ameer & Tyrrell, Andy M., 2022. "Inequalities in photovoltaics modules reliability: From packaging to PV installation site," Renewable Energy, Elsevier, vol. 192(C), pages 805-814.
    7. Sharmarke Hassan & Mahmoud Dhimish, 2022. "Review of Current State-of-the-Art Research on Photovoltaic Soiling, Anti-Reflective Coating, and Solar Roads Deployment Supported by a Pilot Experiment on a PV Road," Energies, MDPI, vol. 15(24), pages 1-24, December.
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