K-means and fuzzy relational eigenvector centrality-based clustering algorithms for defensive islanding

Mohammed Mahdi; V. M.Istemihan Genc

doi:10.1109/ISGTEurope.2016.7856210

K-means and fuzzy relational eigenvector centrality-based clustering algorithms for defensive islanding

Mohammed Mahdi, V. M.Istemihan Genc

Department of Electrical Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coherency based islanding can successfully be applied for the defensive islanding. In this paper, two partitioning methods are proposed, K-means clustering algorithm and fuzzy relational eigenvector centrality-based clustering algorithm. The proposed methods are using the data measured by phasor measurement units to determine the islands to be used in the defensive islanding. The proposed methods are demonstrated on the 16-generator 68-bus power system and their performances are discussed as their results are compared.

Original language	English
Title of host publication	ISGT Europe 2016 - IEEE PES Innovative Smart Grid Technologies, Europe
Publisher	IEEE Computer Society
ISBN (Electronic)	9781509033584
DOIs	https://doi.org/10.1109/ISGTEurope.2016.7856210
Publication status	Published - 2 Jul 2016
Event	2016 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2016 - Ljubljana, Slovenia Duration: 9 Oct 2016 → 12 Oct 2016

Publication series

Name	IEEE PES Innovative Smart Grid Technologies Conference Europe

Conference

Conference	2016 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2016
Country/Territory	Slovenia
City	Ljubljana
Period	9/10/16 → 12/10/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

defensive islanding
FRECCA clustering
k-means clustering
slow coherency

Access to Document

10.1109/ISGTEurope.2016.7856210

Cite this

@inproceedings{0bfdc89984264a3d81b2afb2f5df6afb,

title = "K-means and fuzzy relational eigenvector centrality-based clustering algorithms for defensive islanding",

abstract = "Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coherency based islanding can successfully be applied for the defensive islanding. In this paper, two partitioning methods are proposed, K-means clustering algorithm and fuzzy relational eigenvector centrality-based clustering algorithm. The proposed methods are using the data measured by phasor measurement units to determine the islands to be used in the defensive islanding. The proposed methods are demonstrated on the 16-generator 68-bus power system and their performances are discussed as their results are compared.",

keywords = "defensive islanding, FRECCA clustering, k-means clustering, slow coherency",

author = "Mohammed Mahdi and Genc, {V. M.Istemihan}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2016 ; Conference date: 09-10-2016 Through 12-10-2016",

year = "2016",

month = jul,

day = "2",

doi = "10.1109/ISGTEurope.2016.7856210",

language = "English",

series = "IEEE PES Innovative Smart Grid Technologies Conference Europe",

publisher = "IEEE Computer Society",

booktitle = "ISGT Europe 2016 - IEEE PES Innovative Smart Grid Technologies, Europe",

address = "United States",

}

Mahdi, M & Genc, VMI 2016, K-means and fuzzy relational eigenvector centrality-based clustering algorithms for defensive islanding. in ISGT Europe 2016 - IEEE PES Innovative Smart Grid Technologies, Europe., 7856210, IEEE PES Innovative Smart Grid Technologies Conference Europe, IEEE Computer Society, 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2016, Ljubljana, Slovenia, 9/10/16. https://doi.org/10.1109/ISGTEurope.2016.7856210

K-means and fuzzy relational eigenvector centrality-based clustering algorithms for defensive islanding. / Mahdi, Mohammed; Genc, V. M.Istemihan.
ISGT Europe 2016 - IEEE PES Innovative Smart Grid Technologies, Europe. IEEE Computer Society, 2016. 7856210 (IEEE PES Innovative Smart Grid Technologies Conference Europe).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - K-means and fuzzy relational eigenvector centrality-based clustering algorithms for defensive islanding

AU - Mahdi, Mohammed

AU - Genc, V. M.Istemihan

PY - 2016/7/2

Y1 - 2016/7/2

N2 - Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coherency based islanding can successfully be applied for the defensive islanding. In this paper, two partitioning methods are proposed, K-means clustering algorithm and fuzzy relational eigenvector centrality-based clustering algorithm. The proposed methods are using the data measured by phasor measurement units to determine the islands to be used in the defensive islanding. The proposed methods are demonstrated on the 16-generator 68-bus power system and their performances are discussed as their results are compared.

AB - Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coherency based islanding can successfully be applied for the defensive islanding. In this paper, two partitioning methods are proposed, K-means clustering algorithm and fuzzy relational eigenvector centrality-based clustering algorithm. The proposed methods are using the data measured by phasor measurement units to determine the islands to be used in the defensive islanding. The proposed methods are demonstrated on the 16-generator 68-bus power system and their performances are discussed as their results are compared.

KW - defensive islanding

KW - FRECCA clustering

KW - k-means clustering

KW - slow coherency

UR - http://www.scopus.com/inward/record.url?scp=85017558386&partnerID=8YFLogxK

U2 - 10.1109/ISGTEurope.2016.7856210

DO - 10.1109/ISGTEurope.2016.7856210

M3 - Conference contribution

AN - SCOPUS:85017558386

T3 - IEEE PES Innovative Smart Grid Technologies Conference Europe

BT - ISGT Europe 2016 - IEEE PES Innovative Smart Grid Technologies, Europe

PB - IEEE Computer Society

T2 - 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2016

Y2 - 9 October 2016 through 12 October 2016

ER -

K-means and fuzzy relational eigenvector centrality-based clustering algorithms for defensive islanding

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this