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A deep transfer learning and optimized Krawtchouk moment-based system for fault classification in Solar Panels
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DOI：10.1016/j.compeleceng.2026.111324.png)
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Abstract
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• Deep learning automates fault detection in large-scale solar farms. • Krawtchouk moments focus feature extraction on localized defects. • An optimization algorithm auto-selects optimal moment parameters. • A modified pretrained network with a custom classification head classifies PV fault types. • The model achieves 99.37% and 99.00% balanced accuracy on 2-class and 6-class datasets.
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