Automated Monitoring and Diagnosis of Solar PV Faults Using IoT and AI Technologies

The use of panels as a way to get energy from the sun is becoming more popular. This means we need to find ways to check that these solar panels are working properly and will last a long time. This study is about creating a system to automatically check for problems with panels. We want to use the Internet and smart computers to make this system work. The system will use devices to constantly collect information from the solar panels. These devices will send us real-time data on things, like how electricity the solar panels are making the temperature and how much sunlight they are getting. We are talking about panels and how to make sure they work well. The system will help us keep an eye on the panels and fix any problems that we find. This information is sent to a place where computers use special programs like machine learning and deep learning to look at the information and find problems. The computer programs are taught to find kinds of problems such, as when something is blocking the sun, when it gets dirty when it gets old and when there are electrical issues and they can do this very accurately. The system can also use math to predict when something might go wrong and tell us what to do to fix it before it happens so we can avoid the system stopping and make sure the solar panel system works really well. The implementation of this automated monitoring and diagnosis framework not only enhances the reliability and efficiency of solar PV installations but also reduces maintenance costs and extends the operational lifespan of the equipment. Experimental results and case studies demonstrate the efficacy of the proposed system, highlighting its potential as a valuable tool for the solar energy industry.