Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

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

doi:10.1049/iet-gtd.2018.6315

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19533

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique
Authors:	Barocio, Emilio Korba, Petr Sattinger, Walter Segundo Sevilla, Felix Rafael
et. al:	No
DOI:	10.1049/iet-gtd.2018.6315 10.21256/zhaw-19533
Published in:	IET Generation, Transmission & Distribution
Volume(Issue):	13
Issue:	15
Page(s):	3323
Pages to:	3333
Issue Date:	2019
Publisher / Ed. Institution:	The Institution of Engineering and Technology
ISSN:	1751-8687 1751-8695
Language:	English
Subject (DDC):	621.3: Electrical, communications, control engineering
Abstract:	Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and K -means for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature.
URI:	https://digitalcollection.zhaw.ch/handle/11475/19533
Fulltext version:	Published version
License (according to publishing contract):	CC BY 3.0: Attribution 3.0 Unported
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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Barocio, E., Korba, P., Sattinger, W., & Segundo Sevilla, F. R. (2019). Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique. IET Generation, Transmission & Distribution, 13(15), 3323–3333. https://doi.org/10.1049/iet-gtd.2018.6315

Barocio, E. et al. (2019) ‘Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique’, IET Generation, Transmission & Distribution, 13(15), pp. 3323–3333. Available at: https://doi.org/10.1049/iet-gtd.2018.6315.

E. Barocio, P. Korba, W. Sattinger, and F. R. Segundo Sevilla, “Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique,” IET Generation, Transmission & Distribution, vol. 13, no. 15, pp. 3323–3333, 2019, doi: 10.1049/iet-gtd.2018.6315.

BAROCIO, Emilio, Petr KORBA, Walter SATTINGER und Felix Rafael SEGUNDO SEVILLA, 2019. Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique. IET Generation, Transmission & Distribution. 2019. Bd. 13, Nr. 15, S. 3323–3333. DOI 10.1049/iet-gtd.2018.6315

Barocio, Emilio, Petr Korba, Walter Sattinger, and Felix Rafael Segundo Sevilla. 2019. “Online Coherency Identification and Stability Condition for Large Interconnected Power Systems Using an Unsupervised Data Mining Technique.” IET Generation, Transmission & Distribution 13 (15): 3323–33. https://doi.org/10.1049/iet-gtd.2018.6315.

Barocio, Emilio, et al. “Online Coherency Identification and Stability Condition for Large Interconnected Power Systems Using an Unsupervised Data Mining Technique.” IET Generation, Transmission & Distribution, vol. 13, no. 15, 2019, pp. 3323–33, https://doi.org/10.1049/iet-gtd.2018.6315.