Autonomous power line detection and tracking system using UAVs

Oscar Bowen Schofield; Nicolai Iversen; Emad Ebeid

doi:10.1016/j.micpro.2022.104609

Autonomous power line detection and tracking system using UAVs

Oscar Bowen Schofield^*
, Nicolai Iversen
, Emad Ebeid

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

A large portion of overhead power transmission infrastructure around the world is becoming aged, raising the demand for frequent grid inspections in an efficient, cost-effective manner in order to keep it functional. Drones are a promising solution for infrastructure inspection, however, they require further investigation and development to enable their autonomy for large scale inspection operations. This paper proposes a drone framework based on open-source platforms for autonomous inspection of electrical grid infrastructure. The framework incorporates (1) a cloud service for geo-location data of nearby power pylons; (2) a localisation system for pylon navigation and cable grasping; and (3) a cable following algorithm for cable inspection using onboard sensors. The software functionality of the proposed framework has been validated in a simulation power line environment. Custom drones have been developed for validating the framework and algorithms within an unenergised power line environment.

Original language	English
Article number	104609
Journal	Microprocessors and Microsystems
Volume	94
Number of pages	14
ISSN	0141-9331
DOIs	https://doi.org/10.1016/j.micpro.2022.104609
Publication status	Published - Oct 2022

Funding

This work was supported by the Innovation Fund Denmark, Drones4Energy [grant agreement No 8057-00038A ] and European Union’s Horizon 2020, Drones4Safety [grant agreement No 86111 ]. This work was supported by the Innovation Fund Denmark, Drones4Energy [grant agreement No 8057-00038A] and European Union's Horizon 2020, Drones4Safety [grant agreement No 86111].

Keywords

Autonomous missions
Cloud services
Detection algorithms
Drone framework
Powerline inspection

Documents & Links

10.1016/j.micpro.2022.104609Licence: CC BY

Open Access VersionFinal published version, 17 MBLicence: CC BY

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2 Finished

EU H2020: Drones4Safety
Ebeid, E. S. M. (Coordinator), Lindberg, A. (Project manager), Malle, N. H. (PhD student) & Nyboe, F. F. (PhD student)
01/06/2020 → 31/05/2023
Project: EU
IFD: Drones4Energy
Ebeid, E. S. M. (Coordinator), Lindberg, A. (Project manager), Iversen, N. (Project participant) & Schofield, O. B. (Project participant)
01/12/2018 → 30/11/2021
Project: Research

Cite this

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title = "Autonomous power line detection and tracking system using UAVs",

abstract = "A large portion of overhead power transmission infrastructure around the world is becoming aged, raising the demand for frequent grid inspections in an efficient, cost-effective manner in order to keep it functional. Drones are a promising solution for infrastructure inspection, however, they require further investigation and development to enable their autonomy for large scale inspection operations. This paper proposes a drone framework based on open-source platforms for autonomous inspection of electrical grid infrastructure. The framework incorporates (1) a cloud service for geo-location data of nearby power pylons; (2) a localisation system for pylon navigation and cable grasping; and (3) a cable following algorithm for cable inspection using onboard sensors. The software functionality of the proposed framework has been validated in a simulation power line environment. Custom drones have been developed for validating the framework and algorithms within an unenergised power line environment.",

keywords = "Autonomous missions, Cloud services, Detection algorithms, Drone framework, Powerline inspection",

author = "Schofield, \{Oscar Bowen\} and Nicolai Iversen and Emad Ebeid",

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AU - Ebeid, Emad

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N2 - A large portion of overhead power transmission infrastructure around the world is becoming aged, raising the demand for frequent grid inspections in an efficient, cost-effective manner in order to keep it functional. Drones are a promising solution for infrastructure inspection, however, they require further investigation and development to enable their autonomy for large scale inspection operations. This paper proposes a drone framework based on open-source platforms for autonomous inspection of electrical grid infrastructure. The framework incorporates (1) a cloud service for geo-location data of nearby power pylons; (2) a localisation system for pylon navigation and cable grasping; and (3) a cable following algorithm for cable inspection using onboard sensors. The software functionality of the proposed framework has been validated in a simulation power line environment. Custom drones have been developed for validating the framework and algorithms within an unenergised power line environment.

AB - A large portion of overhead power transmission infrastructure around the world is becoming aged, raising the demand for frequent grid inspections in an efficient, cost-effective manner in order to keep it functional. Drones are a promising solution for infrastructure inspection, however, they require further investigation and development to enable their autonomy for large scale inspection operations. This paper proposes a drone framework based on open-source platforms for autonomous inspection of electrical grid infrastructure. The framework incorporates (1) a cloud service for geo-location data of nearby power pylons; (2) a localisation system for pylon navigation and cable grasping; and (3) a cable following algorithm for cable inspection using onboard sensors. The software functionality of the proposed framework has been validated in a simulation power line environment. Custom drones have been developed for validating the framework and algorithms within an unenergised power line environment.

KW - Autonomous missions

KW - Cloud services

KW - Detection algorithms

KW - Drone framework

KW - Powerline inspection

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DO - 10.1016/j.micpro.2022.104609

M3 - Journal article

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VL - 94

JO - Microprocessors and Microsystems

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ER -

Autonomous power line detection and tracking system using UAVs

Abstract

Funding

Keywords

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Related projects

EU H2020: Drones4Safety

IFD: Drones4Energy

Cite this