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Electrical Fault Localisation Over a Distributed Parameter Transmission Line
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-3329-436X
Eindhoven University of Technology, Control Systems Group, EE Dept., Eindhoven, The Netherlands.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-1835-2963
2023 (English)In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 7088-7093Conference paper, Published paper (Refereed)
Abstract [en]

Motivated by the need to localise faults along electrical power lines, this paper adopts a frequency-domain approach to parameter estimation for an infinite-dimensional linear dynamical system with one spatial variable. Since the time of the fault is unknown, and voltages and currents are measured at only one end of the line, distance information must be extracted from the post-fault transients. To properly account for high-frequency transient behaviour, the line dynamics is modelled directly by the Telegrapher's equation, rather than the more commonly used lumped-parameter approximations. First, the governing equations are non-dimensionalised to avoid ill-conditioning. A closed-form expression for the transfer function is then derived. Finally, nonlinear least-squares optimisation is employed to search for the fault location. Requirements on fault bandwidth, sensor bandwidth and simulation time-step are also presented. The result is a novel end-to-end algorithm for data generation and fault localisation, the effectiveness of which is demonstrated via simulation.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. p. 7088-7093
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-343711DOI: 10.1109/CDC49753.2023.10383452ISI: 001166433805128Scopus ID: 2-s2.0-85184823148OAI: oai:DiVA.org:kth-343711DiVA, id: diva2:1839906
Conference
62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023
Note

Part of ISBN 9798350301243

QC 20240222

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2025-03-19Bibliographically approved

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Selvaratnam, DanielSandberg, Henrik

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