Detection of Soiling on PV Module using Deep Learning

As a clean and sustainable energy source, solar is gaining popularity quickly surpassing other generation methods. However, the accumulation of dirt on surface Photovoltaic panels (PV) significant barrier to harvesting energy. The panel's performance output are negatively impacted by this soiling, which drastically reduces capacity harvest sunlight effectively. Therefore, regular PV module cleaning essential minimise efficiency losses maximize income during system. In work, an intelligent approach for monitoring soiling utilising cutting-edge Artificial Intelligence (AI) methods in order solve problem. AI continues gain become component technological advancements, we employ predictive maintenance strategy using deep learning soiling. Our method utilizes real-time data collection testing, unlike existing models requiring high computational power. We achieved similar results comparing our state-of-the-art computer vision architectures while significantly reducing costs. Experimental demonstrate impressive accuracy rate 97% classifying panels' status. This indicates excellent identifying when require cleaning. proposed can help personnel determine optimal schedules systems. By minimizing power loss saving labour time associated with long-term maintenance, offers tangible benefits overall operation