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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29852
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dc.contributor.authorSandoval Guzmán, Betsy-
dc.contributor.authorBarocio, Emilio-
dc.contributor.authorKorba, Petr-
dc.contributor.authorObushevs, Artjoms-
dc.contributor.authorSegundo Sevilla, Felix Rafael-
dc.date.accessioned2024-02-08T14:34:11Z-
dc.date.available2024-02-08T14:34:11Z-
dc.date.issued2022-
dc.identifier.issn2352-4677de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29852-
dc.description.abstractIn this paper, an innovative data compression methodology based on Tucker tensor decomposition is presented. The proposed approach exploits the benefits of organizing time series in multidimensional arrays (tensors) in order to achieve higher compression ratios of the original data while preserving most of their properties and, in this form, achieving a low reconstruction error. This reorganization of the data into the tensor allows the creation of correlation for heterogeneous data. Furthermore, it reduces the direct relationship between the number of columns and rows with the final size of the compressed file when the data is compressed using algorithms based on matrix-based dimensional reduction techniques, such as the Singular Value Decomposition (SVD). At the same time, the problem of missing data can be overcome since the problem is formulated as an iterative process. To demonstrate the effectiveness of the innovative algorithm proposed, two study cases from electrical networks are presented. First, the compression of real synchrophasor data acquired from one commercial Phasor Measurement Unit (PMU) measuring 3-phase voltages and frequency measurements from the electrical system of Continental Europe is presented, where a significant CR is achieved for apparently no correlated data. Then, an application to real Smart Meter measurements from the electrical utility ERCOT in the USA is also provided. Here the clustering of the data is obtained as an additional outcome of the proposed algorithm. To underline the benefits of the innovative proposed methodology in contrast to other more common decompositions such as SVD, the compression ratios and reconstruction error achieved using these two different techniques are compared. The results indicate that using Tucker decomposition, not only higher compression can be achieved, but also it is demonstrated that for SM data, additional attributes can be obtained, such as clustering.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofSustainable Energy, Grids and Networksde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectData compressionde_CH
dc.subjectTucker decompositionde_CH
dc.subjectTensor decompositionde_CH
dc.subjectSmart meterde_CH
dc.subjectPhasor Measurement Unit (PMU)de_CH
dc.subjectMissing datade_CH
dc.subjectClusteringde_CH
dc.subjectPower systemde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleData compression for advanced monitoring infrastructure information in power systems based on tensor decompositionde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Energiesysteme und Fluid-Engineering (IEFE)de_CH
dc.identifier.doi10.1016/j.segan.2022.100917de_CH
dc.identifier.doi10.21256/zhaw-29852-
zhaw.funding.euNode_CH
zhaw.issue100917de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.volume32de_CH
zhaw.embargo.end2024-09-16de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedElektrische Energiesysteme und Smart Gridsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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Sandoval Guzmán, B., Barocio, E., Korba, P., Obushevs, A., & Segundo Sevilla, F. R. (2022). Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition. Sustainable Energy, Grids and Networks, 32(100917). https://doi.org/10.1016/j.segan.2022.100917
Sandoval Guzmán, B. et al. (2022) ‘Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition’, Sustainable Energy, Grids and Networks, 32(100917). Available at: https://doi.org/10.1016/j.segan.2022.100917.
B. Sandoval Guzmán, E. Barocio, P. Korba, A. Obushevs, and F. R. Segundo Sevilla, “Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition,” Sustainable Energy, Grids and Networks, vol. 32, no. 100917, 2022, doi: 10.1016/j.segan.2022.100917.
SANDOVAL GUZMÁN, Betsy, Emilio BAROCIO, Petr KORBA, Artjoms OBUSHEVS und Felix Rafael SEGUNDO SEVILLA, 2022. Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition. Sustainable Energy, Grids and Networks. 2022. Bd. 32, Nr. 100917. DOI 10.1016/j.segan.2022.100917
Sandoval Guzmán, Betsy, Emilio Barocio, Petr Korba, Artjoms Obushevs, and Felix Rafael Segundo Sevilla. 2022. “Data Compression for Advanced Monitoring Infrastructure Information in Power Systems Based on Tensor Decomposition.” Sustainable Energy, Grids and Networks 32 (100917). https://doi.org/10.1016/j.segan.2022.100917.
Sandoval Guzmán, Betsy, et al. “Data Compression for Advanced Monitoring Infrastructure Information in Power Systems Based on Tensor Decomposition.” Sustainable Energy, Grids and Networks, vol. 32, no. 100917, 2022, https://doi.org/10.1016/j.segan.2022.100917.


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