UAV Power Line Inspection - Current and Prospective Technologies

Electrical transmission inspections are essential to maintaining a healthy and reliable electric grid. Unmanned Aerial Vehicle (UAV) inspections of transmission lines have begun to supplement traditional ground-based and crewed helicopter inspection methods. At the same time, advancements in UAV autonomous navigation capability and flight tracking systems are moving the industry closer to a future self-diagnosing electrical grid. In this panel, we describe our efforts to integrate and test necessary UAV technologies, and our operational tests of them, in the context of a self-diagnosing grid. We also identify and discuss remaining technology gaps that must be addressed to realize it. In particular, automated collection of imagery and other telemetry from distributed UAV deployments could be used to quickly identify asset issues. Furthermore, the ability to automate analysis and remotely interpret inspection data will improve worker efficiency, inspection consistency, overall inspection quality, safety and service reliability. Each speaker will describe their individual and joint efforts. Scope of missions that will be discussed: All panelists have extensive experience in manual, line-of-sight inspection flights for maintenance and fault detection. Additionally, autonomous waypoint-based flights have been conducted jointly using the following technologies: lidar-to-polyhedron preflight processing for obstacle demarcation to determine inspection standoff distance; navigation software to monitor inspection standoff distance and correct the UAV trajectory; telemetry repeater software to send the UAV position to a NASA UTM air traffic management server for tracking in the national airspace; and, compact airborne ultraviolet sensing for transmission line defect detection. Currently we are evaluating several more technologies: intelligent long wave infrared video processing, onboard safety avionics for georeferenced flight termination, robust communication methods, and sensors for obstacle detection. Even if these are successful, regulatory and additional technology gaps remain that prevent the realization of a self-diagnosing grid. We conclude by discussing some of remaining impediments which must be overcome and the additional research needed to embed UAV technology into the power grid to advance its resilience.