Discovering Clusters in Power Networks from Orthogonal Structure of Spectral Embedding

Journal article (2018)

Authors

I. Tyuryukanov Intelligent Electrical Power Grids - EEMCS
M. Popov Intelligent Electrical Power Grids - EEMCS
M.A.M.M. van der Meijden Intelligent Electrical Power Grids - EEMCS
Vladimir Terzija The University of Manchester
Research Group
Intelligent Electrical Power Grids (EEMCS) (TU Delft)
Copyright: © 2018 I. Tyuryukanov, M. Popov, M.A.M.M. van der Meijden, Vladimir Terzija
DOI: https://doi.org/10.1109/TPWRS.2018.2854962
Clustering algorithms Robustness Number of clusters Spectral clustering Voltage control Sparse matrices Adaptive network zone division Partitioning algorithms Power network partitioning Static VAr compensators
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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

Intelligent Electrical Power Grids

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.