Data Quality Analyses for Automatic Aerial Thermography Inspection of PV Power Plants

As the installed capacity of photovoltaic power plants continues its near exponential growth, cost-efficient operation and maintenance strategies become increasingly crucial. Aerial infrared thermography has enabled fast and robust fault detection in utility-scale PV plants. In this article, we explore two key approaches to improve inspection efficiency: increase the flight altitude and deploy swarms of unmanned aerial vehicles. A larger imaging distance expands the field of view but reduces fault detectability and georeferencing accuracy. In this work, we study the tradeoff between inspection efficiency and data quality for automatic fault detection and localization. The YOLO11 machine learning model was trained to detect defects in thermal images, and its performance was evaluated to vary imaging distances and camera pitch angles. Fault detection remained robust up to approximately 80 m, but georeferencing error became the primary limiting factor. Finally, we conduct a UAV swarm-based inspection of a PV plant, integrating automatic fault detection and localization, and compare the results with ground truth data.