Accurate Detection of Bright Spots in Solar Photovoltaic Panels

For optimal performance and safety of photovoltaic (PV) solar panels, early detection of manufacturing defects is very critical. A major concern with PV solar panels is the presence of defects on its surface that show up as “bright spots” in their electro luminescence (EL) images. This defect can lead to malfunctions due to uncontrolled energy production, potentially resulting in short circuits, elevated temperatures, and even fire hazards. In this paper, we propose a robust machine learning (ML) based approach to accurately detect bright spots by optimally splitting the EL images of PV solar panels and engineering novel discriminative features. The proposed method's effectiveness was validated with an impressive F1 Score of 0.96 on real EL images captured directly from a functioning PV solar panel manufacturing unit. This signifies its real-world applicability for reliable bright spot detection in PV solar panels.