This study introduces a robotic system designed to maintain photovoltaic (PV) panel efficiency by removing dust and debris that reduce energy output. The robot uses sensors and actuators to clean panels and adjusts its actions based on real-time environmental data. Engineered for efficiency and energy conservation, it includes cleaning brushes and movement modules. Machine learning techniques convolutional neural networks (CNNs) for detecting dust and reinforcement learning (RL) for optimizing movement paths enhance the robot adaptability. These algorithms enable it to balance dust removal effectiveness, energy use, and time efficiency, optimizing its cleaning strategy for sustainable PV panel maintenance.
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Shah Muhammad Adnan,
shahjee.nnu@gmail.com,
School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing,
China.
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Xu Wensheng,
xuwinsun@126.com,
School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing,
China.
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Muhammad Tahir Zaman,
tzq480@gmail.com,
School of Mechanical Engineering, Southeast University, Nanjing ,
China.
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Muhammad Aurangzeb,
maurangzaib42@gmail.com,
School of Electrical Engineering, Southeast University, Nanjing,
China.
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Fawwad Hassan Jaskani,
,
Department of Advance Research Studies. Islamic Institute of Research & Development, FHJ-CIS, Bahawalpur,
Pakistan.
Shah Muhammad Adnan Xu Wensheng Muhammad Tahir Zaman Muhammad Aurangzeb Fawwad Hassan Jaskani “Advanced Optimization of PV Panel Cleaning Robot Based on Machine Learn Vol. 11 Issue 11 PP. 203-209 November 2024. https://doi.org/10.34259/ijew.24.1111203209.
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, Xu Wensheng, Muhammad Tahir Zaman, Muhammad Aurangzeb, Fawwad Hassan Jaskani
