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
Pages : 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 engineering and electronics
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://ieeexplore.ieee.org/abstract/document/8826732
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

Files in This Item:
File Description SizeFormat 
2019_Korba_Online_coherency_identification_and_stability_condition_IET.pdf5.19 MBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.