Advanced fault detection in photovoltaic panels using enhanced U-Net architectures

Abstract

Fault detection in photovoltaic (PV) panels using thermal images remains a significant challenge due to the complexity of thermal patterns, environmental noise, and the subtle nature of anomalies. This paper introduces an advanced deep learning framework that enhances the U-Net architecture by integrating Residual Blocks, Atrous Spatial Pyramid Pooling (ASPP), and Attention Mechanisms. These enhancements collectively improve feature extraction, contextual understanding, and fault localization, addressing the limitations of traditional segmentation approaches and reducing false positives. Extensive experiments demonstrate that the proposed method significantly outperforms all benchmarked algorithms across key segmentation metrics, including standard U-Net, U-Net with ASPP, and DeepLabV3+. Compared to standard U-Net, the proposed model achieves more than a 29% increase in F1-score and a 62% improvement in Intersection over Union (IoU) while reducing segmentation loss by 71%. Its ability to accurately detect faults under challenging conditions establishes the framework as a state-of-the-art solution for real-time PV monitoring. These results demonstrate the effectiveness of the proposed approach in addressing the challenges of PV fault detection, offering a practical and reliable solution for ensuring the operational performance of renewable energy systems.