機械学習を用いたパターン認識とクラスタリングによるN波脱調を考慮した過渡安定度高速安定判別手法

カテゴリ: 論文誌(論文単位)

グループ名: 【B】電力・エネルギー部門

発行日: 2017/08/01

タイトル(英語): A Fast Screening Method for Transient Stability considering Multi-swing Step-out using Pattern Recognition with Machine Learning and Clustering

著者名: 小林　淳之介（早稲田大学先進理工学研究科電気・情報生命専攻），小柳　唯（早稲田大学先進理工学研究科電気・情報生命専攻），岩本　伸一（早稲田大学先進理工学研究科電気・情報生命専攻）

著者名(英語): Junnosuke Kobayashi (Dept. of Electrical Eng. & Bioscience, Waseda University), Yui Koyanagi (Dept. of Electrical Eng. & Bioscience, Waseda University), Shinichi Iwamoto (Dept. of Electrical Eng. & Bioscience, Waseda University)

キーワード: 過渡安定度，クラスタリング，主成分分析法，電力系統，N波脱調，パターン認識　　transient stability，clustering，principal component analysis，power system，multi-swing step-out，pattern recognition

要約(英語): Recently, online stability monitoring systems have become more important in response to the increasing complexity of power systems. Moreover, there has been a concern about multi-swing step-out due to the Japanese longitudinal power system. In this paper, a fast screening method is proposed considering multi-swing step-out using PCA (principal component analysis). In the proposed method, computers learn patterns of PCA in transient stability data as a form of library. In order to reduce the number of data in the library, k-means method, one of the partitioning-optimization clustering methods, is applied to extract features in the data. In addition, Gaussian mixture model is also applied to extract the feature from a different perspective. Simulations for the proposed method are performed using the IEEJ 10 machine 47 bus system to confirm the validity of the screening method.

本誌: 電気学会論文誌B（電力・エネルギー部門誌） Vol.137 No.8 （2017）

本誌掲載ページ: 559-565 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/ieejpes/137/8/137_559/_article/-char/ja/