電力系統状態推定における不正データ注入攻撃に対するロバストなメータ配置

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2016/03/01

タイトル(英語): A Mathematical Model for Robust Meter Placement Against False Data Injection Attacks on State Estimation in Power Grids

著者名: 渡邊　勇（（一財）電力中央研究所 システム技術研究所／東京工業大学大学院 総合理工学研究科），小野　功（東京工業大学大学院 総合理工学研究科）

著者名(英語): Isamu Watanabe (Central Research Institute of Electric Power Industry/Tokyo Institute of Technology), Isao Ono (Tokyo Institute of Technology)

キーワード: 検知不能攻撃，メータ配置，ロバスト，状態推定，可観測性　　Undetectable attack，Meter placement，Robust，State estimation，Observability

要約(英語): This paper presents a new mathematical model for robust meter placement against false data injection attacks on state estimation in power grids. It has been recently reported that the false data injection attacks can introduce arbitrary errors into the output of the state estimation without being detected. While one of the most promising strategies against attacks is to protect meters, it may not be feasible to protect all meters due to some reasons such as budgetary constraints. It is important to select a subset of meters to be protected so that, even if any k meters are compromised by attackers, we can detect the attacks with the rest of them. Several algebraic approaches have been proposed to determine the minimum required meter set to detect the attacks even if any k meters fail. However, these approaches have a common problem in that the attacks on both of existing and additional meters are not considered. In this paper, to remedy this problem, we propose a new model for robust meter placement against false data injection attacks. Through the numerical experiments with the IEEE test systems, we investigate the total computation time required to obtain the optimal meter placement and the number of required additional meters. As the results, for the case of k=2 and 3, we succeed in obtaining optimal meter placements up to the 300-bus and the 57-bus systems within one hour of computation time, respectively.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.136 No.3 （2016） 特集：機械学習が拓くシステムイノベーション

本誌掲載ページ: 390-400 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/136/3/136_390/_article/-char/ja/