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Title: Data-Driven Partitioning of Power Networks Via Koopman Mode Analysis
Authors: Raak, Fredrik
Susuki, Yoshihiko
Hikihara, Takashi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-0029-4358 (unconfirmed)
Author's alias: 薄, 良彦
引原, 隆士
Keywords: Power system monitoring
spectral graph theory
power network partitioning
coherency identi cation
Issue Date: Jul-2016
Publisher: Institute of Electrical and Electronics Engineers Inc. (IEEE)
Journal title: IEEE Transactions on Power Systems
Volume: 31
Issue: 4
Start page: 2799
End page: 2808
Abstract: This paper applies a new technique for modal decomposition based solely on measurements to test systems and demonstrates the technique's capability for partitioning a power network, which determines the points of separation in an islanding strategy. The mathematical technique is called the Koopman mode analysis (KMA) and stems from a spectral analysis of the so-called Koopman operator. Here, KMA is numerically approximated by applying an Arnoldi-like algorithm recently first applied to power system dynamics. In this paper we propose a practical data-driven algorithm incorporating KMA for network partitioning. Comparisons are made with two techniques previously applied for the network partitioning: spectral graph theory which is based on the eigenstructure of the graph Laplacian, and slow-coherency which identifies coherent groups of generators for a specified number of low-frequency modes. The partitioning results share common features with results obtained with graph theory and slow-coherency-based techniques. The suggested partitioning method is evaluated with two test systems, and similarities between Koopman modes and Laplacian eigenvectors are showed numerically and elaborated theoretically.
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/245666
DOI(Published Version): 10.1109/TPWRS.2015.2464779
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