Inferring Power System Dynamics from Synchrophasor Data using Gaussian Processes

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dc.contributor.authorJalali, Manaen
dc.contributor.authorKekatos, Vassilisen
dc.contributor.authorBhela, Siddharthen
dc.contributor.authorZhu, Haoen
dc.contributor.authorCenteno, Virgilio A.en
dc.date.accessioned2022-02-07T04:34:21Zen
dc.date.available2022-02-07T04:34:21Zen
dc.date.issued2022-01-01en
dc.date.updated2022-02-07T04:34:15Zen
dc.description.abstractSynchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injection at all buses. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging Gaussian processes (GPs). We extend results on learning of a linear time-invariant system using GPs to the multi-input multi-output setup. This is accomplished by decomposing power system swing dynamics into a set of single-input single-output linear systems with narrow frequency pass bands. The proposed learning technique captures time derivatives in continuous time, accommodates data streams sampled at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof collected from an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates of system dynamics. Numerical tests verify that this technique can infer dynamics at non-metered buses, impute and predict synchrophasors, and locate faults under linear and non-linear system models under ambient and fault disturbances.en
dc.description.versionAccepted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TPWRS.2022.3144935en
dc.identifier.eissn1558-0679en
dc.identifier.issn0885-8950en
dc.identifier.issue99en
dc.identifier.orcidKekatos, Vasileios [0000-0003-1127-3285]en
dc.identifier.urihttp://hdl.handle.net/10919/108184en
dc.language.isoenen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectEnergyen
dc.subject0906 Electrical and Electronic Engineeringen
dc.titleInferring Power System Dynamics from Synchrophasor Data using Gaussian Processesen
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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