Photovoltaic module segmentation and thermal analysis tool from thermal images
This work addresses the need for efficient monitoring of photovoltaic systems to ensure proper functioning, but it appears incremental as it applies existing image processing methods to a specific domain without introducing major innovations.
The paper tackled the problem of monitoring large photovoltaic systems by proposing an automatic detection and analysis tool for photovoltaic modules from thermal images, which includes module identification via image processing and statistical temperature analysis, and developed a graphical user interface to provide relevant information.
The growing interest in the use of clean energy has led to the construction of increasingly large photovoltaic systems. Consequently, monitoring the proper functioning of these systems has become a highly relevant issue.In this paper, automatic detection, and analysis of photovoltaic modules are proposed. To perform the analysis, a module identification step, based on a digital image processing algorithm, is first carried out. This algorithm consists of image enhancement (contrast enhancement, noise reduction, etc.), followed by segmentation of the photovoltaic module. Subsequently, a statistical analysis based on the temperature values of the segmented module is performed.Besides, a graphical user interface has been designed as a potential tool that provides relevant information of the photovoltaic modules.