Photovoltaic Fault Detection Using SE-MobileNet: A Lightweight Channel Attention Network

Öz

The global energy sector is rapidly transitioning from fossil fuels to renewable sources, with solar photovoltaic (PV) systems emerging as a leading solution for sustainable electricity generation. However, maintaining the cleanliness and structural integrity of PV panels is crucial for optimal operation. Therefore, early and accurate fault detection is essential to ensure maintained efficiency and system reliability. This study introduces a lightweight deep learning framework, termed SE-MobileNet, for diagnosing PV surface faults such as dust and bird droppings, as well as physical and electrical damage. The proposed model incorporates a Squeeze-and-Excitation (SE) block into a MobileNet backbone to enable channel-wise feature recalibration without imposing a significant computational burden. Experimental results demonstrate that this adaptive channel-wise refinement yields a notable improvement in classification performance, increasing the macro F1-score from 0.776 to 0.837, while outperforming a range of benchmark convolutional and transformer-based models. Furthermore, the model achieves a favorable trade-off between accuracy and computational complexity, with an inference time of 12.73 ms and 78.54 FPS, enabling practical deployment under near real-time conditions in PV monitoring systems.

Anahtar Kelimeler

• Renewable energy
• Solar energy
• Photovoltaic
• PV Fault Detection
• Deep Learning
• Machine Learning

Kaynakça

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