Extraction and modelling of complex power line corridor

Loading...
Thumbnail Image
Files
Munir_Nosheen_Final Thesis_Redacted.pdf
Embargoed until 2025-02-09
File version
Primary Supervisor

Awrangjeb, Mohammad

Other Supervisors

Stantic, Bela

Editor(s)
Date
2024-02-09
Size
File type(s)
Location
License
Abstract

Electricity is crucial in contemporary society, with high-voltage power lines serving as vital components in the power transmission system, facilitating efficient electricity delivery over long distances. The complexity of power line environments, including features like lakes, mountains, and forests, poses challenges for inspection. Meeting the demands for a secure and reliable energy supply requires utility companies to conduct accurate and timely inspections of existing infrastructure and plan for future deployments. Power Line Corridor (PLC) monitoring encompasses electrical components (wires and pylons) and surrounding objects (vegetation). In recent years, Light Detection and Ranging (LiDAR) technology has been favoured for PLC inspection due to its active and weather-independent nature of laser scanning. However, today's corridor mapping practice using LiDAR in industries still remains an expensive manual process that is not suitable for a large-scale and rapid commercial compilation of corridor maps. Additionally, most of the research concerning power line extraction and reconstruction from LiDAR data has focused on single power line spans, or regarded bundle conductors as single conductors, while bundle conductor reconstruction is still a very challenging task. Thus, the objective of this thesis is to build an inspection workflow for PLC based on four major steps: (i) extraction of individual objects for a detailed PLC mapping from large-scale LiDAR data; (ii) extraction of sub-conductors; (iii) robust and precise reconstruction of each sub-conductor, and (iv) vegetation monitoring from airborne LiDAR data. [...]

Journal Title
Conference Title
Book Title
Edition
Volume
Issue
Thesis Type

Thesis (PhD Doctorate)

Degree Program

Doctor of Philosophy (PhD)

School

School of Info & Comm Tech

Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

The author owns the copyright in this thesis, unless stated otherwise.

Item Access Status
Note
Access the data
Related item(s)
Subject

LiDAR

power lines

extraction

Persistent link to this record
Citation