VTechWorks staff will be away for the Independence Day holiday from July 4-7. We will respond to email inquiries on Monday, July 8. Thank you for your patience.
 

Anomaly Detection in Data-Driven Coherency Identification Using Cumulant Tensor

dc.contributor.authorSun, Boen
dc.contributor.authorXu, Yijunen
dc.contributor.authorWang, Qinlingen
dc.contributor.authorLu, Shuaien
dc.contributor.authorYu, Ruizhien
dc.contributor.authorGu, Weien
dc.contributor.authorMili, Lamine M.en
dc.date.accessioned2024-01-22T14:16:39Zen
dc.date.available2024-01-22T14:16:39Zen
dc.date.issued2023-12-04en
dc.description.abstractAs a model reduction tool, coherency identification has been extensively investigated by power researchers using various model-driven and data-driven approaches. Model-driven approaches typically lose their accuracy due to linear assumptions and parameter uncertainties, while data-driven approaches inevitably suffer from bad data issues. To overcome these weaknesses, we propose a data-driven cumulant tensor-based approach that can identify coherent generators and detect anomalies simultaneously. More specifically, it converts the angular velocities of generators into a fourth-order cumulant tensor that can be decomposed to reflect the coherent generators. Also, using co-kurtosis in the cumulant tensor, anomalies can be detected as well. The simulations reveal its excellent performance.en
dc.description.versionAccepted versionen
dc.format.extentPages 1-4en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TPWRS.2023.3338958en
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/10919/117506en
dc.identifier.volumePPen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleAnomaly Detection in Data-Driven Coherency Identification Using Cumulant Tensoren
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

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Yijun-Mili_Anomaly_Detection_in_Data-Driven_Coherency_Identification_Using_Cumulant_Tensor.pdf
Size:
476.07 KB
Format:
Adobe Portable Document Format
Description:
Accepted version
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Plain Text
Description: