Scholarlink

The rapid expansion of solar energy infrastructure necessitates efficient maintenance solutions to mitigate dust-induced efficiency losses. This study proposes an autonomous drone-based technique for cleaning solar panels. Integrated with a wireless sensor network (WSN) for real-time monitoring and targeted cleaning in harsh environments like Iraq. The system employs dual-protocol WSN (Leach/Head) for data transmission, with drones equipped with high-pressure spray mechanisms (water-diesel mixture) and GPS/GLONASS navigation. Results demonstrate significant performance restoration: current (4.17%), voltage (4.17%), and power (5.64%) improvements, with severely soiled panels showing up to 10.77% power recovery. The system reduces manual labor by 70% and achieves 30–35% efficiency restoration in extreme conditions. Seasonal analysis reveals optimal summer performance (100% utilization) and winter limitations (76.7% in December), highlighting opportunities for energy storage integration. The study underscores the potential of autonomous drones in scalable, cost-effective solar farm maintenance while addressing regulatory and technical challenges for broader adoption.

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Enhancing Solar Panel Efficiency in Harsh Climates: An UAV-Integrated WSN Cleaning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Rawnaq Abdoun Naser,
  • Ali Abdul Razzaq Altahir,
  • Ahmed Abdulhadi Ahmed,
  • Mohammad Hussain Alanbari

摘要

The rapid expansion of solar energy infrastructure necessitates efficient maintenance solutions to mitigate dust-induced efficiency losses. This study proposes an autonomous drone-based technique for cleaning solar panels. Integrated with a wireless sensor network (WSN) for real-time monitoring and targeted cleaning in harsh environments like Iraq. The system employs dual-protocol WSN (Leach/Head) for data transmission, with drones equipped with high-pressure spray mechanisms (water-diesel mixture) and GPS/GLONASS navigation. Results demonstrate significant performance restoration: current (4.17%), voltage (4.17%), and power (5.64%) improvements, with severely soiled panels showing up to 10.77% power recovery. The system reduces manual labor by 70% and achieves 30–35% efficiency restoration in extreme conditions. Seasonal analysis reveals optimal summer performance (100% utilization) and winter limitations (76.7% in December), highlighting opportunities for energy storage integration. The study underscores the potential of autonomous drones in scalable, cost-effective solar farm maintenance while addressing regulatory and technical challenges for broader adoption.