Solar Farms and Power Line Inspection using Unmanned Aerial Vehicles

The increasing demand for reliable and sustainable energy necessitates efficient monitoring and maintenance of power transmission lines and solar farms. Traditional inspection methods are labor-intensive, timeconsuming, and often pose safety risks to personnel. This paper explores the use of Unmanned Aerial Vehicles (UAVs) equipped with advanced sensors and artificial intelligence (AI) for automated inspection and fault detection in power infrastructure. The proposed system integrates high-resolution imaging, thermal sensors, and LiDAR to assess structural integrity, detect anomalies, and enhance predictive maintenance. AI-based computer vision algorithms process collected data to identify defects such as conductor sag, insulator damage, panel degradation, and vegetation encroachment. Additionally, a cloud-based analytics platform enables real-time data transmission and decision support. The study evaluates the efficiency, accuracy, and cost-effectiveness of UAV-based inspections compared to conventional methods. Experimental results demonstrate significant improvements in inspection speed, operational safety, and fault diagnosis accuracy. This research contributes to the optimization of energy infrastructure monitoring, supporting the transition to smart and resilient power grids