A YOLO-Driven Automated PV Health Monitoring Cleaning System with Combined IoT Sensor Fusion

The PV solar energy development has already emerged as both framework and keystone to sustainable development in 21st century which is being offered as a boon for a clean pathway of renewable sustainable source amid mounting climate change concerns. With rapid advancement in solar panel efficiency and declining product cost and its widespread adoption in developing nations, its pivotal role in shaping the global economy with transition to low carbon economy changed drastically. However, with the present scenario of rapid climate change and rising global energy demand concerns are being raised regarding the efficiency of solar panels and susceptibility to major factors of environment including dust accumulation, humidity, temperature fluctuation and reduced light intensity. In view of the prevailing condition and present circumstance with alignment to global current trend the following research paper presents and exhibits a novel solution of real time intelligent automated system of PV panel health monitoring and automated cleaning utilizing multimodal data fusion algorithms with embedded IOT control architecture. The proposed solution exhibits panel dirt detection and cleaning system as smart solution towards panel efficiency optimal maintenance through automated monitoring. The following system sites and classifies panel cleanliness in 2 categories namely clean and dirty using lightweight (YOLOv5) model with a webcam feed as aid for capturing and feeding visual input. As complementary to this scenario sensor data with embedded Arduino architecture is being captured with increased reliability and both former and latter data is being fused via Large Language model (LLM) to improve prediction. By this current approach of fusing visual data with sensor input, the system accurately determines when a panel is dirty or not. Upon detection, it alerts the user and can automatically trigger a cleaning mechanism via a relay module. By minimizing manual inspection and mitigating energy loss concerns this method of approach enhances its contribution towards more sustainable solar power systems though timely and intelligent data driven maintenance strategy ultimately promoting sustainable integration at global energy landscape.