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Observability Analysis of a Power System Stochastic Dynamic Model Using a Derivative-Free Approach

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dc.contributor.authorZheng, Zongshengen
dc.contributor.authorXu, Yijunen
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorLiu, Zhigangen
dc.contributor.authorKorkali, Merten
dc.contributor.authorWang, Yuhongen
dc.date.accessioned2024-01-23T18:19:25Zen
dc.date.available2024-01-23T18:19:25Zen
dc.date.issued2021-05-13en
dc.description.abstractServing as a prerequisite to power system dynamic state estimation, the observability analysis of a power system dynamic model has recently attracted the attention of many power engineers. However, because this model is typically nonlinear and large-scale, the analysis of its observability is a challenge to the traditional derivative-based methods. Indeed, the linear-approximation-based approach may provide unreliable results while the nonlinear-technique-based approach inevitably faces extremely complicated derivations. Furthermore, because power systems are intrinsically stochastic, the traditional deterministic approaches may lead to inaccurate observability analyses. Facing these challenges, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only is free of any linear approximations, but also accounts for the stochasticity of the dynamic model while bringing a low implementation complexity. Furthermore, this approach enables us to quantify the degree of observability of a stochastic model, what conventional deterministic methods cannot do. The excellent performance of the proposed method has been demonstrated by performing extensive simulations using a synchronous generator model with IEEE-DC1A exciter and the TGOV1 turbine governor.en
dc.description.versionPublished versionen
dc.format.extentPages 5834-5845en
dc.format.extent12 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TPWRS.2021.3079919en
dc.identifier.eissn1558-0679en
dc.identifier.issn0885-8950en
dc.identifier.issue6en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117620en
dc.identifier.volume36en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000709092000083&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.subjectObservabilityen
dc.subjectAnalytical modelsen
dc.subjectPower system dynamicsen
dc.subjectPower system stabilityen
dc.subjectStochastic processesen
dc.subjectComputational modelingen
dc.subjectPower systemsen
dc.subjectDynamic state estimationen
dc.subjectobservability analysisen
dc.subjectderivative-freeen
dc.subjectpolynomial chaosen
dc.subjectdegree of observabilityen
dc.titleObservability Analysis of a Power System Stochastic Dynamic Model Using a Derivative-Free Approachen
dc.title.serialIEEE Transactions on Power Systemsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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