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Distributed Fault Detection and Isolation Resilient to Network Model Uncertainties
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-5491-4068
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-1835-2963
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
2014 (English)In: IEEE Transactions on Cybernetics, ISSN 2168-2267, E-ISSN 2168-2275, Vol. 44, no 11, 2024-2037 p.Article in journal (Refereed) Published
Abstract [en]

The ability to maintain state awareness in the face of unexpected and unmodeled errors and threats is a defining feature of a resilient control system. Therefore, in this paper, we study the problem of distributed fault detection and isolation (FDI) in large networked systems with uncertain system models. The linear networked system is composed of interconnected subsystems and may be represented as a graph. The subsystems are represented by nodes, while the edges correspond to the interconnections between subsystems. Considering faults that may occur on the interconnections and subsystems, as our first contribution, we propose a distributed scheme to jointly detect and isolate faults occurring in nodes and edges of the system. As our second contribution, we analyze the behavior of the proposed scheme under model uncertainties caused by the addition or removal of edges. Additionally, we propose a novel distributed FDI scheme based on local models and measurements that is resilient to changes outside of the local subsystem and achieves FDI. Our third contribution addresses the complexity reduction of the distributed FDI method, by characterizing the minimum amount of model information and measurements needed to achieve FDI and by reducing the number of monitoring nodes. The proposed methods can be fused to design a scalable and resilient distributed FDI architecture that achieves local FDI despite unknown changes outside the local subsystem. The proposed approach is illustrated by numerical experiments on the IEEE 118-bus power network benchmark.

Place, publisher, year, edition, pages
IEEE Press, 2014. Vol. 44, no 11, 2024-2037 p.
Keyword [en]
Fault diagnosis, multi-agent systems, networked control systems, power systems
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-155775DOI: 10.1109/TCYB.2014.2350335ISI: 000343319700004PubMedID: 25222962Scopus ID: 2-s2.0-84908269867OAI: oai:DiVA.org:kth-155775DiVA: diva2:763323
Funder
Swedish Research Council, 2009-4565 2013-5523Swedish Foundation for Strategic Research Knut and Alice Wallenberg Foundation
Note

QC 20141114

Available from: 2014-11-14 Created: 2014-11-13 Last updated: 2017-12-05Bibliographically approved

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Teixeira, AndréSandberg, HenrikJohansson, Karl H.

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