An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

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dc.contributor.authorXu, Yijunen
dc.contributor.authorValinejad, Jaberen
dc.contributor.authorKorkali, Merten
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorWang, Yajunen
dc.contributor.authorChen, Xiaoen
dc.contributor.authorZheng, Zongshengen
dc.date.accessioned2024-01-23T18:28:07Zen
dc.date.available2024-01-23T18:28:07Zen
dc.date.issued2021-10-20en
dc.description.abstractThe reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.en
dc.description.versionPublished versionen
dc.format.extentPages 2220-2232en
dc.format.extent13 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TPWRS.2021.3121612en
dc.identifier.eissn1558-0679en
dc.identifier.issn0885-8950en
dc.identifier.issue3en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117622en
dc.identifier.volume37en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000784213800051&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.subjectTopologyen
dc.subjectEstimationen
dc.subjectBayes methodsen
dc.subjectPower distributionen
dc.subjectMonte Carlo methodsen
dc.subjectSwitchesen
dc.subjectAdaptation modelsen
dc.subjectTopology estimationen
dc.subjectpower distribution systemen
dc.subjectBayesian inferenceen
dc.subjectadaptive importance samplingen
dc.titleAn Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution Systemen
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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