Projects per year
Abstract
Detecting cracks in solar photovoltaic (PV) modules plays an important role in ensuring their performance and reliability. The development of convolutional neural networks (CNNs) has introduced a game-changing dimension in the detection of defects in PV modules. This paper proposes an automated defect detection method for PV, by leveraging custom-designed CNN to accurately analyse electroluminescence (EL) images, identifying defects such as cracks, mini-cracks, potential induced degradation (PID), and shaded areas. The proposed system achieves a high level of validation accuracy of 98.07%, reducing manual inspection demands, enhancing quality standards, and saving costs. The system was validated in a case study for PV installations faulty with PID, where it identified all defective modules with a high degree of precision of 96.6%, surpassing existing methods. This methodology holds promise for revolutionizing PV industry quality control, improving module reliability, and supporting sustainable solar energy growth.
Original language | English |
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Article number | 119389 |
Number of pages | 13 |
Journal | Renewable Energy |
Volume | 219 |
Early online date | 9 Oct 2023 |
DOIs | |
Publication status | Published - 1 Dec 2023 |
Bibliographical note
This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.Keywords
- Renewable energy
- Solar Energy
- Photovoltaic
- Artificial intelligence
- Machine learning
- Convolutional neural network
Projects
- 1 Active
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Artificial Intelligence-Backed Automation for Detecting Cracks in Solar Cells
Dhimish, M. & Hassan, S.
3/10/22 → 30/09/25
Project: Research project (funded) › Studentship (departmental)