Impacts of infrared thermographic image blurring on UAV inspection efficiency of solar power plants

Abstract

Unmanned aerial vehicles (UAVs) inspecting solar photovoltaic (PV) power plants with infrared (IR) cameras is a well-established method to identify hotspots and other defects that radiate heat. With large PV power plants, the task of inspecting the entire area can be overwhelming if the equipment and planning are inadequate. With so much information in each image, the quality of the images will determine if the inspection is useful or not. In previous work, the uncertainty in UAV navigation system parameters has been analyzed and shown to seriously deteriorate image quality and affect inspection efficiency. However, in this study, the analysis is extended to include the effect of image blurring (called motion blur), resulting from the UAV travelling too fast, obscuring vital details in the video image. The novel analysis shows that motion blur is to be regarded as a key factor limiting data quality and data acquisition efficiency. Thus, a comprehensive PV inspection simulator that analyzes the effect of motion blur combined with the UAV navigation performance, is proposed to assess the complete system performance. The simulator is used to evaluate two levels of navigation precision and three camera setups at three different power plant latitudes. To avoid unacceptable motion blurring in the IR images, the maximum UAV flight velocity is determined for all cases. Subsequently, the maximum data acquisition rate of the overall system is calculated. The simulation results show that the design of a UAV system for PV power plant inspection should include a carefully chosen platform that balances navigation performance and image resolution. The image resolution directly affects the maximum flight velocity of the UAV, caused by motion blurring, thus constraining the inspection time and data acquisition rate.