This paper introduces a diagnostic methodology for photovoltaic panels using I-V curves, enhanced by new techniques combining optimization and classification-based artificial intelligence.
Leveraging the power of IoT sensors and computer vision, a new framework is proposed for defect detection in solar cells as well as solar panels.
Regular maintenance and inspection are vital to extend the lifespan of these systems, minimize energy losses, and protect the environment. This paper presents an innovative explainable
Abstract: This paper presents an Artificial Intelligence solution for fault detection and classification in photovoltaic systems. The proposed tool integrates electrical and visual analysis methods, including I
Photovoltaic module damage frequently manifests as solar cells that become partially or completely disconnected from the circuit. When this occurs, the affected cells cease energy
To gain a deeper understanding of these AI algorithms, we introduce a generic framework of AI-driven systems that can autonomously detect and localise solar panel defects and we analyse
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Solar panels are essential for converting sunlight into electricity. Still, environmental factors can significantly compromise their efficiency and performance, particularly the accumulation
The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is imperative to implement efficient methods for the
In order to tackle this issue, this study presents a PV panel defect detection approach based on the advanced YOLOv11 object detection algorithm. The mosaic augmentation approach is first employed
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