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Corresponding Author

Montaser Abdelsattar

Subject Area

Electrical Engineering

Article Type

Original Study

Abstract

As the use of Photovoltaic (PV) panels as a sustainable energy source grows, there is a need for effective and precise techniques to monitor and manage these systems. Conventional techniques for identifying faults in PV panels, such as manual inspections, require a significant amount of effort and are susceptible to mistakes made by humans. This study introduces an innovative automated method that utilizes image processing techniques implemented using the OpenCV library to identify panel faults, namely hotspots, which are important indications of possible failures. The study approach combines grayscale conversion, histogram analysis, and adaptive thresholding to accurately detect and evaluate abnormalities in panel integrity. The method starts with the conversion of Red, Blue, and Green (RGB) images to grayscale in order to decrease computing complexity. Histogram analysis then follows to evaluate the pixel intensity distribution. The study then employs adaptive thresholding to accurately delineate affected regions, thereby improving the overall precision of damage identification. By reducing time and expense, this automated technology enhances both the precision of detection and the efficiency of existing approaches. The results indicate that automated image processing is a scalable and efficient technique for regular maintenance of PV panels, which has the ability to avert significant damage and optimize panel performance. Future research endeavors will involve the integration of sophisticated Deep Learning (DL) algorithms such as ResNet and YOLO to improve system detection capabilities and automation, thereby facilitating the development of more robust energy infrastructure

Keywords

Automated Detection, Fault Detection, Image Analysis, Image Processing, Photovoltaic Panels, Renewable Energy

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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