Real-Time Environmental Cognition and Sag Estimation of Transmission Lines Using UAV Equipped With 3-D Lidar System

Transmission lines (TLs) are prone to frequent failures owing to their exposure to extreme environments. It is essential to monitor these failures and repair the TLs on time; moreover, their health must be accurately estimated to ensure reliability and safety. Therefore, this study proposes an intelligent monitoring method with novel sensors deployed on a UAV. Specifically, this paper presents a method to not only cognize TLs and their environments in real time but also estimate the sag by combining the measured point-cloud data from a Lidar with the flight information of a UAV. Environmental cognition in real time addresses coordinate transformation and probabilistic downsampling for voxelized mapping, thus ensuring limited hardware requirement. A robust random-sample consensus is introduced to effectively extract point-cloud data for TLs, thereby accurately estimating the sag. Field tests confirmed the accuracy of the proposed method and demonstrated the effectiveness and advantages of the smart environmental cognition system. One flight was sufficient for estimating the sag on both sides of the TLs, demonstrating the economic feasibility of the proposed method. Considering the inherent advantages of the diagnostics and prognostics of a UAV, the proposed method has potential applications in the reliable operation and proactive maintenance of TLs.