Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41543

Automated Anomaly Detection in Distribution Grids Using uPMU Measurements

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dc.contributor.author Jamei, Mahdi
dc.contributor.author Scaglione, Anna
dc.contributor.author Roberts, Ciaran
dc.contributor.author Stewart, Emma
dc.contributor.author Peisert, Sean
dc.contributor.author McParland, Chuck
dc.contributor.author McEachern, Alex
dc.date.accessioned 2016-12-29T01:14:12Z
dc.date.available 2016-12-29T01:14:12Z
dc.date.issued 2017-01-04
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41543
dc.description.abstract The impact of Phasor Measurement Units (PMUs) for providing situational awareness to transmission system operators \ has been widely documented. Micro-PMUs (uPMUs) \ are an emerging sensing technology that can provide similar \ benefits to Distribution System Operators (DSOs), enabling a \ level of visibility into the distribution grid that was previously \ unattainable. In order to support the deployment of these \ high resolution sensors, the automation of data analysis and \ prioritizing communication to the DSO becomes crucial. In this \ paper, we explore the use of uPMUs to detect anomalies on \ the distribution grid. Our methodology is motivated by growing \ concern about failures and attacks to distribution automation \ equipment. The effectiveness of our approach is demonstrated \ through both real and simulated data.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Intrusion Detection
dc.subject Anomaly Detection
dc.subject Micro-Phasor Measurement Unit
dc.subject Distribution Grid
dc.title Automated Anomaly Detection in Distribution Grids Using uPMU Measurements
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2017.388
Appears in Collections: Resilent Networks Minitrack


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