A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation

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dc.contributor.authorNetto, Marcosen
dc.contributor.authorKrishnan, Venkaten
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorSusuki, Yoshihikoen
dc.contributor.authorZhang, Yingchenen
dc.date.accessioned2024-01-22T15:47:29Zen
dc.date.available2024-01-22T15:47:29Zen
dc.date.issued2019-08-01en
dc.description.abstractThis paper combines model-based and data-driven methods to develop a hierarchical, decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE after a principled sparse selection. This selection allows us to shrink the dimension of the problem at the upper level and hence significantly speed up the computational time. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system, and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.en
dc.description.versionPublished versionen
dc.format.extentPages 1-5en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/PESGM40551.2019.8973772en
dc.identifier.eissn1944-9933en
dc.identifier.isbn9781728119816en
dc.identifier.issn1944-9925en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117520en
dc.identifier.volume2019-Augusten
dc.language.isoenen
dc.publisherIEEEen
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.titleA Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimationen
dc.title.serialIEEE Power and Energy Society General Meetingen
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherConference Proceedingen
pubs.finish-date2019-08-08en
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
pubs.start-date2019-08-04en

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