设计基于物联网的太阳能电池阵清洁系统,提高性能和效率

Muazzam Ghafoor, Arslan Ahmed Amin, Muhammad Shoaib Khalid
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摘要

太阳能光伏(PV)电池板因表面积聚污垢和环境污染物而导致性能下降。随着太阳能电池板安装数量的增加,提高其效率对于优化电力输出和减少对额外电池板的需求变得更加重要。清洁光伏电池板可以提高其效率,而采用尖端技术的自动清洁系统可以提高清洁效果。本文提出了一种利用消息队列遥测传输协议来提高太阳能电池板效率的系统。此外,该系统还利用了物联网技术的力量来实现清洁过程的自动化。该系统集成了物联网传感器、执行器和通信模块,全部由 ESP-32 控制。我们使用 Arduino 和 Visual Studio Code 软件对微控制器进行编程。Adafruit IO 对系统进行控制和监控。用户可以使用 Adafruit 面板在全自动和手动模式之间切换。该系统采用直流电机、尼龙电刷、金属框架、继电器模块、降压转换器和升压转换器。在 20 安培充电控制器和 12 V/2.5 Ah 电池的帮助下,10 瓦太阳能电池板为系统供电。Adafruit 仪表板可完全控制和监控所开发的系统。通过自动清洁过程和利用实时数据,该系统最大限度地提高了太阳能发电效率,减少了停机时间。该系统将经过测试的 30 W 太阳能电池板的效率提高了 30%。
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Design of IoT-based solar array cleaning system with enhanced performance and efficiency
Solar photovoltaic (PV) panels suffer from a reduction in performance due to dirt and environmental pollutants accumulating on their surface. As the number of solar panels installed grows, boosting their efficiency becomes more crucial to optimizing electricity output while decreasing the need for additional panels. Cleaning the PV panels can increase their efficiency, and an automated cleaning system with cutting-edge technologies can improve cleaning effectiveness. This article proposes a system that utilizes the Messaging Queuing Telemetry Transport protocol to enhance the efficiency of solar panels. Also, the system harnesses the power of IoT technology to automate the cleaning process. The system incorporates IoT-enabled sensors, actuators, and communication modules, all controlled by ESP-32. We programed the microcontroller using the software Arduino and Visual Studio Code. The Adafruit IO controls and monitors the system. Users can switch between fully automatic and manual modes using the Adafruit dashboard. The system utilizes DC motors, nylon brushes, a metal frame, a relay module, a buck converter, and a boost converter. A 10W solar panel powers the system with the help of a 20 A charge controller and a 12 V/2.5 Ah battery. The Adafruit dashboard fully controls and monitors the developed system. By automating the cleaning process and leveraging real-time data, the system maximizes solar power generation efficiency, minimizing downtime. The system increases the efficiency of tested 30 W solar panels by 30%.
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