A Data-Driven Koopman Approach for Power System Nonlinear Dynamic Observability Analysis

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dc.contributor.authorXu, Yijunen
dc.contributor.authorWang, Qinlingen
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorZheng, Zongshengen
dc.contributor.authorGu, Weien
dc.contributor.authorLu, Shuaien
dc.contributor.authorWu, Zhien
dc.date.accessioned2024-01-23T18:33:07Zen
dc.date.available2024-01-23T18:33:07Zen
dc.date.issued2023-08-15en
dc.description.abstractA prerequisite to dynamic state estimation of a stochastic nonlinear dynamic model of a power system is its observability analysis. However, due to the model nonlinearity, the traditional methods either suffer from a poor estimation accuracy if a linear approximation is performed or yield an extremely complicated procedure if the Lie-derivative method is applied to a large-scale power system. To address these weaknesses, we propose a new data-driven Koopman-based observability method by revealing the link that exists between the Koopman operator and the Lie-derivative in the Koopman canonical coordinates. This enables the proposed data-driven method not only to be fully <italic>derivative-free</italic>, which alleviates its implementation complexity but also overcomes the model nonlinearity and inaccuracy of the system. Furthermore, as an important byproduct, the proposed observability analysis scheme provides a valuable guide for the selection of the <italic>observables</italic> of the Koopman operator, which is a major difficulty for the application of this operator. Finally, we demonstrate the excellent performance of the proposed method on several IEEE standard test systems.en
dc.description.versionAccepted versionen
dc.format.extentPages 1-15en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TPWRS.2023.3305404en
dc.identifier.eissn1558-0679en
dc.identifier.issn0885-8950en
dc.identifier.issue99en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117625en
dc.identifier.volumePPen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleA Data-Driven Koopman Approach for Power System Nonlinear Dynamic Observability Analysisen
dc.title.serialIEEE Transactions on Power Systemsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
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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