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TPWRS.2015.2464779.pdf | 1.46 MB | Adobe PDF | 見る/開く |
タイトル: | Data-Driven Partitioning of Power Networks Via Koopman Mode Analysis |
著者: | Raak, Fredrik Susuki, Yoshihiko https://orcid.org/0000-0003-4701-1199 (unconfirmed) Hikihara, Takashi https://orcid.org/0000-0002-0029-4358 (unconfirmed) |
著者名の別形: | 薄, 良彦 引原, 隆士 |
キーワード: | Power system monitoring spectral graph theory power network partitioning coherency identi cation |
発行日: | Jul-2016 |
出版者: | Institute of Electrical and Electronics Engineers Inc. (IEEE) |
誌名: | IEEE Transactions on Power Systems |
巻: | 31 |
号: | 4 |
開始ページ: | 2799 |
終了ページ: | 2808 |
抄録: | 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. |
著作権等: | © 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(出版社版): | 10.1109/TPWRS.2015.2464779 |
出現コレクション: | 学術雑誌掲載論文等 |
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