ENHANCING PHOTOVOLTAIC PANEL EFFICIENCY WITH AN IOT-ENABLED ROBOTIC CLEANING SYSTEM – A COMPREHENSIVE REVIEW
Journal Title: Engineering and Technology Journal - Year 2023, Vol 8, Issue 06
Abstract
The maintenance and cleaning of photovoltaic panels is critical to ensure maximum energy output and prolong their lifespan. However, manual cleaning of large-scale solar farms is time-consuming, labor-intensive, and expensive. To address this challenge, this paper proposes an IoT-based robotic cleaner for efficient monitoring and cleaning of photovoltaic panels. The proposed robotic cleaner is equipped with a camera, sensors, and a cleaning mechanism. The camera captures real-time images of the solar panels, which are analyzed by an AI-based algorithm to detect and locate dirt, debris, and other obstructions. The sensors measure environmental factors such as temperature, humidity, and light intensity, which are used to optimize the cleaning schedule and ensure the safety of the cleaning operation. The cleaning mechanism of the robotic cleaner is based on a high-pressure water jet that removes dirt and debris without damaging the solar panels. The water is supplied by an onboard tank and can be heated or cooled as required. The cleaning process is automated and can be controlled remotely through a web-based interface. The proposed IoT-based robotic cleaner offers several benefits compared to manual cleaning. It reduces labor costs, minimizes the risk of injury to workers, and improves the efficiency of cleaning operations. Moreover, it ensures consistent and high-quality cleaning, which leads to increased energy output and prolongs the lifespan of the solar panels. Overall, this paper presents a novel approach to automate the cleaning of photovoltaic panels using IoT and robotics technologies. The proposed solution has the potential to revolutionize the maintenance of solar farms and contribute to the development of sustainable and clean energy systems.
Authors and Affiliations
Mohammed Ghadhban Ahmed Anmar Aidi Sharif
Keywords
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- EP ID EP717586
- DOI 10.47191/etj/v8i6.07
- Views 57
- Downloads 0
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