Using Drones for Thermal Imaging Photography and Building 3D Images to Analyze the Defects of Solar Modules

Abstract

In this research, drones were used to capture thermal images and detect different types of failure of solar modules, and MATLAB® image analysis was also conducted to evaluate the health of the solar modules. The processes included image acquisition and transmission by drone, grayscale conversion, filtering, 3D image construction, and analysis. The analyzed targets were the solar modules installed on buildings. The results showed that the employment of drones to monitor solar module farms could significantly improve inspection efficiency. Moreover, by combining the mean and median filtering techniques, an innovative box filtering method was successfully created. Additionally, this study compared the differences between the mean, median, and box filtering techniques, and proved that the 3D image improved by box filtering is a more convenient and accurate way to check the health of solar modules than the mean and median filtering methods. In addition, this new method can simplify the maintenance process, as it helps maintenance personnel to determine whether to replace the solar modules on site, achieving the goal of power generation efficiency enhancement. It is worth noting that 3D image recognition technology can enhance the clarity of thermal images, thereby providing maintenance personnel with better defect diagnosis capability. It is also able to provide the temperature value of the defect zone, and to indicate the scale of defects through the cumulative temperature chart, so the 3D image is qualified as a quantitative and qualitative indicator. The analysis of the transmitted image is innovative that it not only can locate the defect area of the module, but also can display the temperature of the module, providing more information for maintenance personnel.

Implementation of Renewable Energy from Solar Photovoltaic (PV) Facilities in Peru: A Promising Sustainable Future

“…Photogrammetry software reconstructs RBG orthomosaic and thermal maps and adjusts the location using ground control points. After location and rebuilding adjustments, maps are created and then linked to a GIS, where solar energy facility field operators can rapidly identify anomalies and detect solar panel failures [38]. It is expected that in the coming years, the evolution of artificial intelligence (AI) and machine learning (ML) technologies as part of the Industry 4.0 paradigm will contribute to the solar energy industry, for example, in global solar irradiation modeling/prediction and solar power production estimates, among other areas [40,41].…”

Internal Detection of Ground-Penetrating Radar Images Using YOLOX-s with Modified Backbone

“…Loss cls + λLoss reg + Loss obj N pos (6) where Loss cls represents classification loss, Loss reg represents regression loss, Loss obj represents object loss λ represents the balance coefficient of localization loss, which is set to 5.0 in the source code; N pos represents the number of Anchor Points that are classified as positive samples.…”

“…Kuo-Chien Liao used thermal images of solar panels to detect various types of faults in solar modules. He combined mean filtering and median filtering techniques to create an innovative box filtering method [6].…”

Detection and analysis of deteriorated areas in solar PV modules using unsupervised sensing algorithms and 3D augmented reality

“… [40] 2020 Deep Learning (Mask R-CNN, UNet, FPNet, LinkNet) UAV-based thermal imaging 0.741 and 0.841 of values on Jaccard and Dice indices Binary segmentation, Multi-class segmentation, Dataset improvement, Real-time measurements [37] 2021 Deep learning approach, Unified model, Various modalities (thermal to visual images), Various energy installations – Accuracy of 0.79 in surface damage detection Limited to surface damage detection, specific energy installations considered. [44] 2022 Drones, Thermal images, MATLAB image analysis, Image acquisition, Grayscale conversion, Filtering, 3D image construction Solar modules installed on buildings Improved inspection efficiency, Enhanced defect diagnosis capability Limited to thermal image-based defects, potential dependency on image quality. [45] 2023 Ghost Convolution, BottleneckCSP, Tiny target prediction head, YOLOv5, Feature Pyramid Network (FPN), Path Aggregation Network (PAN) PV panel surface images Improved accuracy in tiny defect detection, Enhanced model inference speed Limited to PV panel surface defect detection, potential scalability limitations.…”