Data analytic tool for clustering identification based on dimensionality reduction of frequency measurements

Segundo Sevilla, Felix Rafael; Korba, Petr; Barocio Espejo, Emilio; Chavez, Hector; Sattinger, Walter

doi:10.1109/SGSMA.2019.8784626

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19544

Publication type:	Conference paper
Type of review:	Peer review (abstract)
Title:	Data analytic tool for clustering identification based on dimensionality reduction of frequency measurements
Authors:	Segundo Sevilla, Felix Rafael Korba, Petr Barocio Espejo, Emilio Chavez, Hector Sattinger, Walter
et. al:	No
DOI:	10.1109/SGSMA.2019.8784626 10.21256/zhaw-19544
Proceedings:	2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)
Conference details:	2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), College Station, Texas, USA, 20-23 May 2019
Issue Date:	2019
Publisher / Ed. Institution:	IEEE
ISBN:	978-1-7281-1607-5
Language:	English
Subjects:	Power system dynamics; Data analytics; Coherency group; Frequency problem
Subject (DDC):	621.3: Electrical, communications, control engineering
Abstract:	This 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.
Further 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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/19544
Fulltext version:	Accepted version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Energy Systems and Fluid Engineering (IEFE)
Appears in collections:	Publikationen School of Engineering

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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.