A Low-Rank Tensor Train Approach for Electric Vehicle Load Data Reconstruction Using Real Industrial Data

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dc.contributor.authorSun, Boen
dc.contributor.authorXu, Yijunen
dc.contributor.authorGu, Weien
dc.contributor.authorCai, Huihuangen
dc.contributor.authorLu, Shuaien
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorYu, Wenwuen
dc.contributor.authorWu, Zhien
dc.date.accessioned2024-12-13T13:51:45Zen
dc.date.available2024-12-13T13:51:45Zen
dc.date.issued2024-09-30en
dc.description.abstractAs electric vehicles (EVs) gain popularity, their interaction with the power system cannot be overlooked. Therefore, there is a growing need for accurate EV load data to facilitate precise operation and control in power systems. However, in practice, due to the high cost of high-frequency measurement devices and limited data storage capacity, only low-resolution metered EV data are available. To address this, this paper proposed a tensor completion-based method for EV load data reconstruction. More specifically, we first reformulate the load data as high-dimensional tensors and consider unknown data to be recovered as missing entries. Subsequently, we leverage the low-rank properties of high-dimensional data to perform tensor completion. To achieve this, two optimization formulations are proposed: a nuclear norm minimization algorithm based on singular value thresholding (SVT) and a tensor rank approximation algorithm via parallel matrix factorization. Both approaches are based on the tensor train (TT) rank, thanks to its well-balanced matricization scheme. This enables us to cost-effectively reconstruct high-resolution EV data using only low-resolution measurements. Simulation results using real industrial data reveal the excellent performance of the proposed methods.en
dc.description.versionAccepted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TSG.2024.3471077en
dc.identifier.eissn1949-3061en
dc.identifier.issn1949-3053en
dc.identifier.issue99en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/123790en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleA Low-Rank Tensor Train Approach for Electric Vehicle Load Data Reconstruction Using Real Industrial Dataen
dc.title.serialIEEE Transactions on Smart Griden
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen

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