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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19544
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dc.contributor.authorSegundo Sevilla, Felix Rafael-
dc.contributor.authorKorba, Petr-
dc.contributor.authorBarocio Espejo, Emilio-
dc.contributor.authorChavez, Hector-
dc.contributor.authorSattinger, Walter-
dc.date.accessioned2020-02-25T12:31:16Z-
dc.date.available2020-02-25T12:31:16Z-
dc.date.issued2019-
dc.identifier.isbn978-1-7281-1607-5de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/19544-
dc.description© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.de_CH
dc.description.abstractThis work presents a data analytic tool for clustering analysis based on Dimensionality Reduction (DR) of power system measurements. The proposed method is applied to frequency measurements of the ENTSO-E dynamic model of continental Europe and the results are compared with other conventional DR approaches. After considerable reduction of the raw measurements, a phasor metric for identification of coherency groups of generators is proposed. The recommended measure stands for its simple implementation, interpretation and fast computation. To illustrate the effectiveness of the clustering approach and the coherency of the metrics, a particular study case following the outage of a representative generation unit in France is presented.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPower system dynamicsde_CH
dc.subjectData analyticsde_CH
dc.subjectCoherency groupde_CH
dc.subjectFrequency problemde_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleData analytic tool for clustering identification based on dimensionality reduction of frequency measurementsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Energiesysteme und Fluid-Engineering (IEFE)de_CH
dc.identifier.doi10.1109/SGSMA.2019.8784626de_CH
dc.identifier.doi10.21256/zhaw-19544-
zhaw.conference.details2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), College Station, Texas, USA, 20-23 May 2019de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.title.proceedings2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)de_CH
zhaw.funding.snf173628de_CH
zhaw.author.additionalNode_CH
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Segundo Sevilla, F. R., Korba, P., Barocio Espejo, E., Chavez, H., & Sattinger, W. (2019). Data analytic tool for clustering identification based on dimensionality reduction of frequency measurements. 2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA). https://doi.org/10.1109/SGSMA.2019.8784626
Segundo Sevilla, F.R. et al. (2019) ‘Data analytic tool for clustering identification based on dimensionality reduction of frequency measurements’, in 2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA). IEEE. Available at: https://doi.org/10.1109/SGSMA.2019.8784626.
F. R. Segundo Sevilla, P. Korba, E. Barocio Espejo, H. Chavez, and W. Sattinger, “Data analytic tool for clustering identification based on dimensionality reduction of frequency measurements,” in 2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), 2019. doi: 10.1109/SGSMA.2019.8784626.
SEGUNDO SEVILLA, Felix Rafael, Petr KORBA, Emilio BAROCIO ESPEJO, Hector CHAVEZ und Walter SATTINGER, 2019. Data analytic tool for clustering identification based on dimensionality reduction of frequency measurements. In: 2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA). Conference paper. IEEE. 2019. ISBN 978-1-7281-1607-5
Segundo Sevilla, Felix Rafael, Petr Korba, Emilio Barocio Espejo, Hector Chavez, and Walter Sattinger. 2019. “Data Analytic Tool for Clustering Identification Based on Dimensionality Reduction of Frequency Measurements.” Conference paper. In 2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA). IEEE. https://doi.org/10.1109/SGSMA.2019.8784626.
Segundo Sevilla, Felix Rafael, et al. “Data Analytic Tool for Clustering Identification Based on Dimensionality Reduction of Frequency Measurements.” 2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), IEEE, 2019, https://doi.org/10.1109/SGSMA.2019.8784626.


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