A Space-Air-Ground Monitoring Method for Electrical Fitting Hazard in Transmission Line

• Published in: 2025 IEEE 8th International Electrical and Energy Conference (CIEEC) pp. 2012 - 2016
• Main Authors: Ren, Xuejun, Sun, Chenhao, Zhou, Quan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.05.2025
• Subjects: association pattern recognition; electrical fitting failure; Fitting; hazard area prediction; Hazards; High-voltage techniques; Inspection; Maintenance; Manuals; Monitoring; Pattern recognition; Power system reliability; Power transmission lines; space-air ground network
• DOI: 10.1109/CIEEC64805.2025.11116177

Due to the expansion of high-voltage transmission and transformation projects, the harsh environmental conditions of the transmission lines, and the diversity of renewable energy distribution, the safe and stable operation of high-voltage and ultra-high-voltage lines has become particularly important. The issue lies in the fact that electrical fittings, key components of transmission lines, are exposed to harsh outdoor environments for long periods, subject to external factors such as sunlight, rain, ice accumulation, bird damage, and temperature variations, as well as internal factors like materials and tension. This exposure inevitably leads to failures such as deformation, corrosion, cracks, and fractures, which can affect the normal operation of the transmission lines. Currently, the inspection of electrical fittings heavily relies on manual labor, leading to high economic costs and significant safety risks. By integrating aerial drones with monitoring devices on ground-based towers, a more efficient navigation and remote sensing system can be constructed. This system can broaden its functional coverage and provide new methods for maintenance personnel. Ergo, a space-air-ground integrated network-based hazard assessment for electrical fittings method is proposed, combining association pattern recognition to unify various impacts or risks into a set of comprehensive security warning levels. An empirical case study is conducted to validate the performance of this method.