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Discovering Clusters in Power Networks from Orthogonal Structure of Spectral Embedding. / Tyuryukanov, Ilya; Popov, Marjan; van der Meijden, Mart; Terzija, Vladimir.

In: IEEE Transactions on Power Systems, Vol. 33, No. 6, 2018, p. 6441-6451.

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Tyuryukanov, Ilya ; Popov, Marjan ; van der Meijden, Mart ; Terzija, Vladimir. / Discovering Clusters in Power Networks from Orthogonal Structure of Spectral Embedding. In: IEEE Transactions on Power Systems. 2018 ; Vol. 33, No. 6. pp. 6441-6451.

BibTeX

@article{5b0c71ce23d7464fb15cedb7beb64f08,
title = "Discovering Clusters in Power Networks from Orthogonal Structure of Spectral Embedding",
abstract = "This paper presents an integrated approach to partition similarity graphs, the task that arises in various contexts in power system studies. The approach is based on orthogonal transformation of row-normalized eigenvectors obtained from spectral clustering to closely fit the axes of the canonical coordinate system. We select the number of clusters as the number of eigenvectors that allows the best alignment with the canonical coordinate axes, which is a more informative approach than the popular spectral eigengap heuristic. We show a link between the two relevant methods from the literature and on their basis construct a robust and time-efficient algorithm for eigenvector alignment. Furthermore, a graph partitioning algorithm based on the use of aligned eigenvector columns is proposed, and its efficiency is evaluated by comparison with three other methods. Lastly, the proposed integrated approach is applied to the adaptive reconfiguration of secondary voltage control (SVC) helping to achieve demonstrable improvements in control performance.",
keywords = "adaptive network zone division, Clustering algorithms, number of clusters, Partitioning algorithms, Power network partitioning, Robustness, Sparse matrices, spectral clustering, Static VAr compensators, Voltage control",
author = "Ilya Tyuryukanov and Marjan Popov and {van der Meijden}, Mart and Vladimir Terzija",
note = "Accepted Author Manuscript",
year = "2018",
doi = "10.1109/TPWRS.2018.2854962",
language = "English",
volume = "33",
pages = "6441--6451",
journal = "IEEE Transactions on Power Systems",
issn = "0885-8950",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "6",

}

RIS

TY - JOUR

T1 - Discovering Clusters in Power Networks from Orthogonal Structure of Spectral Embedding

AU - Tyuryukanov, Ilya

AU - Popov, Marjan

AU - van der Meijden, Mart

AU - Terzija, Vladimir

N1 - Accepted Author Manuscript

PY - 2018

Y1 - 2018

N2 - This paper presents an integrated approach to partition similarity graphs, the task that arises in various contexts in power system studies. The approach is based on orthogonal transformation of row-normalized eigenvectors obtained from spectral clustering to closely fit the axes of the canonical coordinate system. We select the number of clusters as the number of eigenvectors that allows the best alignment with the canonical coordinate axes, which is a more informative approach than the popular spectral eigengap heuristic. We show a link between the two relevant methods from the literature and on their basis construct a robust and time-efficient algorithm for eigenvector alignment. Furthermore, a graph partitioning algorithm based on the use of aligned eigenvector columns is proposed, and its efficiency is evaluated by comparison with three other methods. Lastly, the proposed integrated approach is applied to the adaptive reconfiguration of secondary voltage control (SVC) helping to achieve demonstrable improvements in control performance.

AB - This paper presents an integrated approach to partition similarity graphs, the task that arises in various contexts in power system studies. The approach is based on orthogonal transformation of row-normalized eigenvectors obtained from spectral clustering to closely fit the axes of the canonical coordinate system. We select the number of clusters as the number of eigenvectors that allows the best alignment with the canonical coordinate axes, which is a more informative approach than the popular spectral eigengap heuristic. We show a link between the two relevant methods from the literature and on their basis construct a robust and time-efficient algorithm for eigenvector alignment. Furthermore, a graph partitioning algorithm based on the use of aligned eigenvector columns is proposed, and its efficiency is evaluated by comparison with three other methods. Lastly, the proposed integrated approach is applied to the adaptive reconfiguration of secondary voltage control (SVC) helping to achieve demonstrable improvements in control performance.

KW - adaptive network zone division

KW - Clustering algorithms

KW - number of clusters

KW - Partitioning algorithms

KW - Power network partitioning

KW - Robustness

KW - Sparse matrices

KW - spectral clustering

KW - Static VAr compensators

KW - Voltage control

UR - http://www.scopus.com/inward/record.url?scp=85049806433&partnerID=8YFLogxK

U2 - 10.1109/TPWRS.2018.2854962

DO - 10.1109/TPWRS.2018.2854962

M3 - Article

VL - 33

SP - 6441

EP - 6451

JO - IEEE Transactions on Power Systems

T2 - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

SN - 0885-8950

IS - 6

ER -

ID: 46622057