Ultra-High Voltage Line UAV Inspection Technology

Hui Xu, Libo Liu, Jiafeng Chen, Guangze Zhu, Yanchen Cui, Zhicheng Wang, Lei Yu, Jiaqiang Sun, Kangxian Tang and Jilei Cai

GBP Proceedings Series, 2025, vol. 7, issue None, 15-23

Abstract: Ultra-high voltage (UHV) transmission lines, as the backbone of national energy transmission, are crucial for their safe and stable operation. However, traditional manual inspection methods suffer from low efficiency, high labor intensity, significant safety risks, and limitations due to terrain and climate conditions, as well as observation blind spots, making them inadequate for the intelligent operation and maintenance (O&M) needs of modern power grids. The rapid advancement of drone technology has brought revolutionary breakthroughs in UHV line inspections. This article aims to systematically explore the application of drone inspection technology in UHV scenarios, focusing on its technical principles, system composition, core advantages, and challenges. The research covers key technologies such as drone platform selection, the functions and applications of critical mission payloads, high-precision positioning and navigation, autonomous obstacle avoidance, strong electromagnetic environment adaptability, multi-sensor fusion, and intelligent defect recognition based on deep learning. Through case studies, it demonstrates that drone technology significantly enhances inspection efficiency and coverage, reduces operational risks and costs, and achieves precise and intelligent detection. Although there are still challenges in endurance, adaptability to complex environments, data processing efficiency, and standardization, drone inspection technology represents the inevitable direction for the intelligent O&M of UHV lines. Its deep integration with cutting-edge technologies like artificial intelligence, digital twins, and cluster collaboration will usher in a new era of transmission line O&M.

Keywords: ultra-high voltage transmission line; UAV inspection; power system security; intelligent operation and maintenance; fault detection; multi-sensor fusion (search for similar items in EconPapers)
Date: 2025
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