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IJIEE
Articles
Published: 2025-03-24

Understanding Defect Detection in Solar Cells: Challenges and Deep Learning Approaches with Electroluminescence Imaging

Department of Computer Science & Engineering, Stanley College of Engineering and Technology for Women, Abids,Hyderabad,Telangana
Department of Computer Science & Engineering, Stanley College of Engineering and Technology for Women, Abids,Hyderabad,Telangana
Department of Computer Science & Engineering, Stanley College of Engineering and Technology for Women, Abids,Hyderabad,Telangana
Department of Computer Science & Engineering, Stanley College of Engineering and Technology for Women, Abids,Hyderabad,Telangana
Solar Cells Photovoltaic Systems Electroluminescence Imaging Defect Detection Deep Learning Vision Transformers

Abstract

Solar energy has gained considerable attention in recent years due to the pressing need to minimize carbon emissions and fight climate change. Photovoltaic (PV) solar cells are crucial in the harvesting of solar energy, but their efficiency and longevity potential is subjected to the effects of defects (e.g., microcracks, finger interruptions). Identifying and diagnosing these defects is crucial for keeping systems running in optimal performance. Although current systems can achieve good performance using Convolutional Neural Networks (CNNs) with encoder-decoder structures, they do not perform well when modelling long-range dependencies, e.g., in situations with complex backgrounds or ambiguous pseudo-defects. Transformers are good at capturing global dependencies at the cost of losing fine-grained local structural information. In detail, this paper presents a hybrid model of CNNs and transformers to eliminate the limitations of existing SSL techniques by leveraging the strength of EfficientNet and transformers architectures. This design integrates local and global features to enhance defect detection capabilities, offering a robust solution for improving the performance and reliability of photovoltaic systems.

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How to Cite

Neeli Swathi, Chitigala Mouleeshwari, Marupaka Manisha Rani, & B V Ramana Murthy. (2025). Understanding Defect Detection in Solar Cells: Challenges and Deep Learning Approaches with Electroluminescence Imaging. International Journal of Interpreting Enigma Engineers (IJIEE), 2(1), 38–43. Retrieved from https://ejournal.svgacademy.org/index.php/ijiee/article/view/139

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