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Hybrid CNN-EML model for fault diagnosis in electroluminescence images of photovoltaic cells

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  • Drir, Nadia
  • Chekired, Fathia
  • Mellit, Adel
  • Blasuttigh, Nicola

Abstract

The quality inspection of solar module manufacturing is essential to guarantee photovoltaic (PV) power plants' steady. This paper presents the development of an innovative hybrid model that combines convolutional neural networks (CNN) with ensemble machine learning (EML) algorithms. The integration of these approaches was employed in order to develop a ranking weight voting system to the features extracted by the CNN model, by combining three fundamental algorithms: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). The advanced CNN-Ensemble Machine Learning (CNN-EML) technique is applied to a dataset of electroluminescence (EL) images featuring the nine most important and frequent defects. The results demonstrated that techniques based on the CNN-EML provide superior classification accuracy, effectively addressing the challenge of diagnosing faults in PV module manufacturing. The CNN-EML model achieved a significant accuracy of 94 % in classification of different defects, outperforming CNN algorithm-based methods in the proposed comparative analysis.

Suggested Citation

  • Drir, Nadia & Chekired, Fathia & Mellit, Adel & Blasuttigh, Nicola, 2025. "Hybrid CNN-EML model for fault diagnosis in electroluminescence images of photovoltaic cells," Renewable Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:renene:v:250:y:2025:i:c:s0960148125010055
    DOI: 10.1016/j.renene.2025.123343
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    References listed on IDEAS

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    1. Akram, M. Waqar & Li, Guiqiang & Jin, Yi & Chen, Xiao & Zhu, Changan & Zhao, Xudong & Khaliq, Abdul & Faheem, M. & Ahmad, Ashfaq, 2019. "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, Elsevier, vol. 189(C).
    2. Pratt, Lawrence & Govender, Devashen & Klein, Richard, 2021. "Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation," Renewable Energy, Elsevier, vol. 178(C), pages 1211-1222.
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    1. Ren Fang-Rong & Wu Tao-Feng & Zhang Qing-Qing, 2025. "Ecological–environmental transformation efficiency in China: regional disparities, modeling challenges, and prospects for long-term sustainability governance," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-20, December.
    2. Li, Xinming & Wang, Yiyan & Xing, Jinduo & Wang, Yanxue, 2026. "Causal graph inference with adaptive dynamic structure learning for mechanism-oriented fault diagnosis in dynamic industrial systems," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).

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