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This paper presents a hybrid vision transformer model for classifying defects in photovoltaic modules using infrared thermographic imaging. It compares the performance of a Vision Transformer (ViT) model and five hybrid ViT models combined with various machine learning algorithms, achieving high accuracy rates of 99.30% and 98.73%, respectively. The study emphasizes the importance of early fault detection in enhancing the efficiency and reliability of photovoltaic systems.

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univ.pc30

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EN