Defensive islanding using self-organizing maps neural networks and hierarchical clustering

Mohammed Mahdi; Istemihan Genc

doi:10.1109/PTC.2015.7232427

Defensive islanding using self-organizing maps neural networks and hierarchical clustering

Mohammed Mahdi, Istemihan Genc

Department of Electrical Engineering

Istanbul Technical University

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

11 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 objective is 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 new partitioning methods, hierarchical clustering and clustering using self-organizing maps neural networks, have been proposed to determine the clusters 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	2015 IEEE Eindhoven PowerTech, PowerTech 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479976935
DOIs	https://doi.org/10.1109/PTC.2015.7232427
Publication status	Published - 31 Aug 2015
Event	IEEE Eindhoven PowerTech, PowerTech 2015 - Eindhoven, Netherlands Duration: 29 Jun 2015 → 2 Jul 2015

Publication series

Name	2015 IEEE Eindhoven PowerTech, PowerTech 2015

Conference

Conference	IEEE Eindhoven PowerTech, PowerTech 2015
Country/Territory	Netherlands
City	Eindhoven
Period	29/06/15 → 2/07/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

defensive islanding
hierarchical clustering
self-organizing maps neural networks
slow coherency

Access to Document

10.1109/PTC.2015.7232427

Cite this

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title = "Defensive islanding using self-organizing maps neural networks and hierarchical clustering",

abstract = "Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The objective is 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 new partitioning methods, hierarchical clustering and clustering using self-organizing maps neural networks, have been proposed to determine the clusters 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, hierarchical clustering, self-organizing maps neural networks, slow coherency",

author = "Mohammed Mahdi and Istemihan Genc",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Eindhoven PowerTech, PowerTech 2015 ; Conference date: 29-06-2015 Through 02-07-2015",

year = "2015",

month = aug,

day = "31",

doi = "10.1109/PTC.2015.7232427",

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Mahdi, M & Genc, I 2015, Defensive islanding using self-organizing maps neural networks and hierarchical clustering. in 2015 IEEE Eindhoven PowerTech, PowerTech 2015., 7232427, 2015 IEEE Eindhoven PowerTech, PowerTech 2015, Institute of Electrical and Electronics Engineers Inc., IEEE Eindhoven PowerTech, PowerTech 2015, Eindhoven, Netherlands, 29/06/15. https://doi.org/10.1109/PTC.2015.7232427

Defensive islanding using self-organizing maps neural networks and hierarchical clustering. / Mahdi, Mohammed; Genc, Istemihan.
2015 IEEE Eindhoven PowerTech, PowerTech 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7232427 (2015 IEEE Eindhoven PowerTech, PowerTech 2015).

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

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AB - Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The objective is 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 new partitioning methods, hierarchical clustering and clustering using self-organizing maps neural networks, have been proposed to determine the clusters 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.

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KW - hierarchical clustering

KW - self-organizing maps neural networks

KW - slow coherency

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Y2 - 29 June 2015 through 2 July 2015

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Defensive islanding using self-organizing maps neural networks and hierarchical clustering

Abstract

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Keywords

Access to Document

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