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Hierarchical Spectral Clustering of Power Grids

Lookup NU author(s): Professor Janusz Bialek



This is the final published version of an article that has been published in its final definitive form by IEEE, 2014.

For re-use rights please refer to the publisher's terms and conditions.


A power transmission system can be represented by a network with nodes and links representing buses and electrical transmission lines, respectively. Each line can be given a weight, representing some electrical property of the line, such as line admittance or average power flow at a given time. We use a hierarchical spectral clustering methodology to reveal the internal connectivity structure of such a network. Spectral clustering uses the eigenvalues and eigenvectors of a matrix associated to the network, it is computationally very efficient, and it works for any choice of weights. When using line admittances, it reveals the static internal connectivity structure of the underlying network, while using power flows highlights islands with minimal power flow disruption, and thus it naturally relates to controlled islanding. Our methodology goes beyond the standard k-means algorithm by instead representing the complete network substructure as a dendrogram. We provide a thorough theoretical justification of the use of spectral clustering in power systems, and we include the results of our methodology for several test systems of small, medium and large size, including a model of the Great Britain transmission network.

Publication metadata

Author(s): Sanchez-Garcia R, Fennelly M, Norris S, Wright N, Niblo G, Brodzki J, Bialek J

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Power Systems

Year: 2014

Volume: 29

Issue: 5

Pages: 2229-2237

Print publication date: 01/09/2014

Online publication date: 17/03/2014

Acceptance date: 09/02/2014

Date deposited: 19/07/2019

ISSN (print): 0885-8950

ISSN (electronic): 1558-0679

Publisher: IEEE


DOI: 10.1109/TPWRS.2014.2306756


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