Hassan, Sharmarke and Dhimish, Mahmoud (2023) Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection. Renewable Energy. 119389. ISSN 0960-1481
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | 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 |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 13 Oct 2023 23:20 |
Last Modified: | 14 Mar 2025 00:10 |
Published Version: | https://doi.org/10.1016/j.renene.2023.119389 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.renene.2023.119389 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204188 |
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Licence: CC-BY 2.5