Design and Application of a UAV Autonomous Inspection System for High-Voltage Power Transmission Lines

TLDR

Human‑based inspections cannot keep pace with the expanding power grid, and current UAV systems suffer from limited endurance, high operational demands, low autonomy, poor identification accuracy, and slow report generation. The authors design an autonomous UAV inspection system that incorporates path planning, sliding‑mode control, mobile inspection schemes, and intelligent fault diagnosis. The system retrieves grid data from a cloud database, computes positional displacements, generates inspection paths, applies a reference‑model sliding‑mode controller for stability, streams captured images to the cloud in real time, executes a mobile inspection program, transfers equipment during flight, and uses a YOLOX‑based detector that raises mAP0.5:0.95 by 2.22 percentage points for bird‑nest detection. Flight tests show the system markedly increases inspection efficiency, shortens inspection cycles, cuts manpower and material costs, and successfully integrates the high‑precision detector into high‑voltage line inspections.

Abstract

As the scale of the power grid continues to expand, the human-based inspection method struggles to meet the needs of efficient grid operation and maintenance. Currently, the existing UAV inspection system in the market generally has short endurance power time, high flight operation requirements, low degree of autonomous flight, low accuracy of intelligent identification, slow generation of inspection reports, and other problems. In view of these shortcomings, this paper designs an intelligent inspection system based on self-developed UAVs, including autonomous planning of inspection paths, sliding film control algorithms, mobile inspection schemes and intelligent fault diagnosis. In the first stage, basic data such as latitude, longitude, altitude, and the length of the cross-arms are obtained from the cloud database of the power grid, while the lateral displacement and vertical displacement during the inspection drone operation are calculated, and the inspection flight path is generated independently according to the inspection type. In the second stage, in order to make the UAV’s flight more stable, the reference-model-based sliding mode control algorithm is introduced to improve the control performance. Meanwhile, during flight, the intelligent UAV uploads the captured photos to the cloud in real time. In the third stage, a mobile inspection program is designed in order to improve the inspection efficiency. The transfer of equipment is realized in the process of UAV inspection. Finally, to improve the detection accuracy, a high-precision object detector is designed based on the YOLOX network model, and the improved model increased the mAP0.5:0.95 metric by 2.22 percentage points compared to the original YOLOX_m for bird’s nest detection. After a large number of flight verifications, the inspection system designed in this paper greatly improves the efficiency of power inspection, shortens the inspection cycle, reduces the investment cost of inspection manpower and material resources, and successfully fuses the object detection algorithm in the field of high-voltage power transmission lines inspection.