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Sahil Dipak Rahane,
B. B. Kadam,
- Student, Department of Electrical Engineering, Sanjivani College of Engineering Kopargaon, Maharashtra, India
- Student, Department of Electrical Engineering, Sanjivani College of Engineering Kopargaon, Maharashtra, India
Abstract
In this project, we build a system that helps detect and monitor faults in solar panels using IoT and Deep Learning. Nowadays, solar panels are used everywhere, but they often do not deliver full power due to dust, cracks, or overheating. So, we need a system that can automatically check the panel’s health. Our proposed idea uses IoT sensors to collect data such as temperature, voltage, current, and light intensity from the solar panel. This data is sent to the cloud or server using the internet. After that, Deep Learning models like CNNs and LSTMs are used to check for any faults or hotspots in the panel. Some work also uses a drone and a thermal camera to capture an image of the panel and easily identify the problem area. Many research papers show that this type of system can find error early and save more energy. It also helps with predictive maintenance, meaning problems are detected before failure. Using machine learning and AI, the system can learn and improve with time. In short, the IoT- and AI-based solar panel monitoring system is smarter, faster, and lower-cost. It reduces human labor and improves power generation from solar energy. This study mainly focuses on developing a simple, automated method to check solar panel health using new technology.
Keywords: PV systems, fault detection, UAV inspection, neural networks, IoT, predictive maintenance, renewable energy.
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Journal of Alternate Energy Sources & Technologies
| Volume | 17 | |
| 03 | ||
| Received | 30/07/2026 | |
| Accepted | 14/10/2026 | |
| Published | 30/10/2026 | |
| Publication Time | 92 Days |