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Turkish Journal of Electrical Engineering and Computer Sciences

Author ORCID Identifier

MAHE ZABIN: 0009-0002-6611-5698

Abstract

Solar panels are becoming very essential in providing sustainable energy but they are usually affected by defects on the surface like dust, snow, bird droppings, physical damages and electrical faults which interfere with their performance. These faults must be identified accurately and in a timely manner to enhance energy efficiency, lower the maintenance cost, and supplement the traditional manual methods of inspection which are labor-intensive, time-consuming and subject to human errors in judgment. The most common methods, such as traditional CNNs and hybrid architectures tend to be less accurate, less explainable and cannot be properly evaluated to be deployed in real time. In order to overcome these issues, we proposed SwinDeiTViT an ensemble model that fuses Swin-Tiny and DeiT-Small vision transformers using a soft voting layer. The framework uses sophisticated preprocessing and data augmentation in order to increase feature visibility and model generalization. SwinDeiTViT reaches an overall accuracy of 99.31%, cross-validation accuracy of 98.41%, F1-scores of approximately 1.0 and a Cohen’s Kappa of 0.9915, with low inference latency that is compatible with edge deployment on a varied set of 826 solar panel images expanded to 4320 samples. Also, Grad-CAM visualizations provide transparent interpretability by highlighting regions contributing to predictions.The proposed framework can provide a functional, operational solution to real-time monitoring of solar panels, which is useful to enhance manual inspection, and be more robust, reliable, and explainable compared to a baseline model.

DOI

10.55730/1300-0632.4194

Keywords

Solar panels, vision transformers, defect detection, ensemble learning, Grad-CAM

First Page

624

Last Page

642

Publisher

The Scientific and Technological Research Council of Türkiye (TÜBİTAK)

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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