Unmanned Aerial Vehicle Inspection and Status Assessment Technology for Transmission Lines Based on Digital Twins

With the continuous improvement of the intelligence level of the power grid, traditional manual inspection methods are no longer able to meet the high requirements for real-time and precision in modern transmission line operation and maintenance. Therefore, this article proposes a transmission line inspection and status assessment technology system that integrates digital twin and drone technology. This system is based on multi-source perception, constructing a three-dimensional digital mapping model of transmission lines, realizing real-time interconnection of virtual and real data, and combining with improved YOLOv5-Mv3 deep learning algorithm to achieve automatic recognition and state quantification evaluation of fault images. The system architecture includes a physical entity layer, a perceptual transport layer, a digital twin modeling layer, and a visual decision layer, forming a “perception cognition decision” closed-loop mechanism. The experiment was conducted on a 220kV line, and the verification results showed that the average accuracy of system defect recognition reached 86.2%, the deviation between health assessment and manual scoring did not exceed ± 0.09, the inspection cycle was shortened by 65%, and the operation and maintenance efficiency and intelligence level were significantly improved. This system has good engineering practicality and provides effective support for the digital transformation of the power grid.