An IoT-based quality inspection robot for measuring conductor sag and detecting defects in ultra-high voltage overhead transmission lines

Abstract Aiming to solve the urgent problem of sag measurement in constructing ultra-high voltage (UHV) power transmission lines non-line-of-sight conditions, we have developed an internet of things-based quality inspection robot for UHV overhead transmission line conductors to assist in construction. This robot not only improves the accuracy of sag measurement but also possesses the function of detecting and locating line defects. Utilizing sensors, it monitors sag, environment, and operating conditions in real-time and uploads the results to a cloud platform. The proposed lightweight YOLOv5s-SGSW algorithm, deployed on NVIDIA Jetson TX2, detects transmission line defects with 81.9% precision at 35.62 FPS. The integration of autonomous positioning algorithms and defect detection locates the defects on the transmission line. The relative errors are 0.25% for the X -coordinate and 2.07% for the Y -coordinate of the defect localization. A new tension-based sag compensation method is proposed to compensate for the sag error caused by the robot’s weight, with experimental results showing an error of 1.3%. This indicates that the robot can meet the expected design goals and satisfy the practical engineering requirements.