Iot-Based Solar Panels Automated Cleaning System Using Arduino Microcontroller - Nurul Liyana, Ya’akup (Universiti Malaysia Sarawak (UNIMAS), 2023)
This project provides a description of the design and construction of an automated cleaning system for solar panels. Such a system has the potential to improve the effectiveness of solar panels and the amount of energy that they produce. The primary objective of this project is to create an automated solar panel cleaning system that is both low in cost and reliable, and it will be built on the Internet of Things (IoT). The accumulation of dust on solar panels is the source of the issue, which in turn hinders the panels' ability to work effectively and efficiently. The presence of dust decreases the quantity of sunlight that is able to reach the photovoltaic cells, which in turn results in a lower amount of power being generated. This problem is especially widespread in settings that are open and have a high potential for the collection of dust. As a result, an automatic cleaning system for solar panels is going to be developed as part of this project. The dust sensor served as a framework for the design of this cleaning system. A communication protocol is provided by the ESP32 Wi-Fi module, while the Arduino UNO board serves as the primary control unit. The proposed system is made up of three different parts: a sensor that detects and processes the amount of dust, a microcontroller that activates a windscreen wiper system, and a controller that manages the cleaning procedure. These systems are simple to control and may be monitored using an intuitive user interface designed for mobile devices such as smartphones. This project can make use of an Internet of Things (IoT)-based automatic cleaning system for solar panels. Using Internet connections, the monitoring of this automated cleaning system can be done through ThingSpeak. Installing mobile applications on mobile phones, such as the Blynk application, gives users the ability to monitor and control the cleaning system of solar panels. Data analysis is used in this study to investigate how the performance of solar panels is affected by the deposition of dust. The findings indicate that the automated cleaning system is responsible for an improvement in solar panel production of more than 10%, resulting in an increase in productivity of 15.04% from 46.97W to 75.10W. In conclusion, the goals of this study were effectively accomplished through the development of an Internet of Things-based automatic cleaning system for solar panels. In subsequent studies, it is crucial to address these constraints and investigate potential new lines of inquiry.