Electroluminescence image-based defective photovoltaic (solar) cell detection using a modified deep convolutional neural network

Electroluminescence image-based defective photovoltaic (solar) cell detection using a modified deep convolutional neural network


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Abstract. Electroluminescence (EL) imaging of photovoltaic solar cells can detect and classify solar panel faults. This method allows technicians and manufacturers to identify defective panels that may affect performance and longevity. However, noise in EL images and solar cell silicon granularity make this process difficult. The paper presents an automated deep-learning framework to identify faulty and normal solar cells from images. Xception, a popular CNN network, is modified to reduce complexity and solve overfitting issues. Few separable convolution layers were removed from the original Xception network, and lateral dropout layers were added. The proposed deep CNN is tested on ELVP. To balance two classes, images are augmented with two rotations and dimensional shifting. Finally, the proposed model is compared to a pretrained CNN network and leading methods. The quantitative analysis showed that the model performed better than previous methods, with 94.382% accuracy, 92% precision, 95.12% recall rate, and 93.53% F1 score. Module fault identification helps with maintenance planning. Solar energy’s widespread adoption and growth as a renewable and sustainable power source may result.

Renewable Energy, Photovoltaic Solar Panels, Deep Convolution Neural Network, Image Classification

Published online 7/15/2024, 8 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Hiren MEWADA, L. SYAMSUNDAR, Hiren Kumar THAKKAR, Miral DESAI, Electroluminescence image-based defective photovoltaic (solar) cell detection using a modified deep convolutional neural network, Materials Research Proceedings, Vol. 43, pp 13-20, 2024

DOI: https://doi.org/10.21741/9781644903216-2

The article was published as article 2 of the book Renewable Energy: Generation and Application

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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