Ground Based Inspection for Overhead Transmission Line Sag

Daniel Mitchell, Jamie Blanche, Marc Desmulliez, Sumanth Pavuluri, David Flynn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Upscaling of the energy network represents a challenge on the resilience of the electricity network and grid to ensure that there is electricity available to meet demand. Cost-effective maintenance of high power electrical lines are of critical importance and overhead linesmen face several safety concerns during inspection of catenary lines which include working in remote areas, at height and facing adverse weather conditions. Whilst some technological advancements have been made via robotic sensing and in-situ sensing systems, these proposed solutions are inadequate for hazardous weather conditions and still include risks to workers. This article presents preliminary results obtained using a Frequency Modulated Continuous Wave (FMCW) radar sensing employed as an alternative solution for the measurement of sag in high power transmission lines. The results indicate that the FMCW radar sensor can detect multiple overhead lines within a single scan and the relevant overhead line sag. Results when compared against a laser distance meter provide an average accuracy of 95.5%.

Original languageEnglish
Title of host publication29th IEEE International Conference on Electronics, Circuits and Systems 2022
PublisherIEEE
ISBN (Electronic)9781665488235
DOIs
Publication statusPublished - 12 Dec 2022
Event29th IEEE International Conference on Electronics, Circuits and Systems 2022 - Glasgow, United Kingdom
Duration: 24 Oct 202226 Oct 2022

Conference

Conference29th IEEE International Conference on Electronics, Circuits and Systems 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period24/10/2226/10/22

Keywords

  • Catenary Line
  • FMCW radar
  • microwave sensing
  • power line sag
  • pylon maintenance

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Instrumentation

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