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Real-time fault diagnosis for large-scale nonlinear power networks
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0003-1835-2963
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2013 (English)In: 2013 IEEE 52nd Annual Conference on Decision and Control (CDC), IEEE conference proceedings, 2013, 2340-2345 p.Conference paper (Refereed)
Abstract [en]

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission lines. Transmission line protection is an important issue in power system engineering because a large portion of power system faults is occurring in transmission lines. This paper presents a novel technique to detect, isolate and identify the faults on transmissions using only a small number of observations. We formulate the problem of fault diagnosis of nonlinear power network into a compressive sensing framework and derive an optimisation-based formulation of the fault identification problem. An iterative reweighted ℓ1-minimisation algorithm is finally derived to solve the detection problem efficiently. Under the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles of nonlinear power networks.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 2340-2345 p.
, IEEE Conference on Decision and Control. Proceedings, ISSN 0743-1546
Keyword [en]
Electric fault currents, Electric lines, Electric network analysis, Iterative methods, Problem solving, Transmission line theory, Automatic fault diagnosis, Compressive sensing, Fault identifications, Large-scale power networks, Power system engineerings, Power system fault, Real-time fault diagnosis, Transmission line protection, Nonlinear equations
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-151010DOI: 10.1109/CDC.2013.6760230ISI: 000352223502108ScopusID: 2-s2.0-84902338294ISBN: 978-146735717-3OAI: diva2:746200
52nd IEEE Conference on Decision and Control, CDC 2013, 10 December 2013 through 13 December 2013, Florence, Italy

QC 20140912

Available from: 2014-09-12 Created: 2014-09-12 Last updated: 2015-12-08Bibliographically approved

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Sandberg, Henrik
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Automatic Control
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