Automatic Cleaning and Maintenance System for Photovoltaic Power Generation Based on Integrated Machine Learning Algorithm

When large-scale photovoltaic power generation is put into use, it is necessary to consider how to keep photovoltaic panels as high as possible. However, the efficiency of photovoltaic panels is not static, its efficiency by light, temperature, shadow, dirt and other factors. It is therefore necessary to design a photovoltaic power generation maintenance system that continuously monitors the power generation environment, provides early warning of potential power generation inefficiencies, and automatically intervenes to maximize power generation efficiency. The maintenance system consists of hardware and software components, hardware connected to sensors and cleaning facilities, access to daily data. The software transmits the collected data to an intelligent algorithm that analyzes it to determine whether today's cleaning needs have not been captured. The experimental data verify the validity of the learning model.