Lucas Leonardo M. Carvalho 
, Jaqueline Neves Silva , Ronney A. M. Boloy , Leandro Almeida Vasconcelos , Gabriel Matos Araujo & Milena Faria Pinto
Abstract: The reliable operation of photovoltaic (PV) systems depends on the early detection of failures such as hotspots, which are localized overheating regions typically caused by partial shading, cell mismatch, soiling, or internal defects. In these regions, affected cells operate in reverse bias and dissipate energy as heat instead of generating electricity, leading to efficiency losses, accelerated material degradation, and, in severe cases, irreversible damage or fire hazards. Conventional inspection methods, including visual evaluation and electrical testing, are time-consuming, labor-intensive, and unsuitable for large-scale solar plants. The use of Unmanned Aerial Vehicles (UAVs) equipped with thermal cameras has emerged as a promising alternative for autonomous inspection, enhancing operational efficiency and reducing human risks. In this work, we propose a hotspot detection approach based on a U-Net segmentation network applied to thermal infrared imagery of PV modules. The model was trained on a dataset that combined publicly available and laboratory-acquired images, utilizing preprocessing and augmentation strategies to enhance robustness. Experimental results demonstrate that the proposed method effectively identifies hotspot regions with precise delineation of their morphology and spatial distribution, outperforming bounding-box-based approaches such as YOLO in terms of fine-grained localization. This level of detail is crucial for predictive maintenance, as it enables the accurate measurement of hotspot size and shape. The findings highlight the potential of integrating UAV-based thermal inspection with deep learning segmentation models as a scalable and reliable solution for autonomous PV system monitoring and maintenance.
Keywords: Deep Learning, hotspot detection, infrared imagery, U-Net, photovoltaic systems.
DOI code: 10.21528/lnlm-vol23-no2-art5
PDF file: vol23-no2-art5.pdf
BibTex file: vol23-no2-art5.bib