Solar Photovoltaic (PV) systems are increasingly vital for enhancing energy security worldwide. However, their efficiency and power output can be significantly reduced by hotspots and snail trails,
In order to detect photovoltaic panels in some special environments, a part of the dataset is selected for image processing, and the photovoltaic panel scene in some special scenarios is
Power output will decline when foreign objests covered on PV panels. In this paper a system dsigned to detect the power output decline caused by foreign objests in different situations effectively.
This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules.
To mitigate background interference in large-scale images, we propose a Dynamically Adaptive and High-Efficiency Small Object Detection Network in Infrared Thermographic Images,
To tackle this challenge, we propose an Adaptive Complementary Fusion (ACF) module designed to intelligently integrate spatial and channel information.
This paper designed and implemented a detection system of foreign objects coverage on PV panels which mainly composed by power detection module, irradiance detection module, temperature...
To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional
During the power generation process of photovoltaic panels, a series of failures may occur. Common faults such as foreign object blocking, cracking, and hot spot heating, greatly impact the power
Foreign object shading is not a failure in the physical sense of PV modules, but it is one of the most critical problems affecting the performance of PV generation systems.
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