油入ケーブル接続部の線形サポートカーネルマシンによる異常判定

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: 2014/08/01

タイトル(英語): Diagnostic Criteria of Oil-Filled Cable Joint Boxes based on Linear Support Kernel Machines

著者名: 篠原　靖志（（一財）電力中央研究所　システム技術研究所），嘉屋　健（関西電力（株）電力システム技術センター），松谷　悠司（関西電力（株）電力システム技術センター）

著者名(英語): Yasusi Sinohara (System Engineering Labs., Central Research Institute of Electric Power Industry), Takeshi Kaya (The Kansai Electric Power Co. Inc.), Yuji Matsuya (The Kansai Electric Power Co. Inc.)

キーワード: OFケーブル，油中ガス分析，サポートベクターマシン，多カーネル学習　　Oil Filled Cables，Gas-In-Oil Analysis，Support Vector Machines，Multiple Kernel Learning

要約(英語): Japanese electric power companies widely use the gas-in-oil based diagnostic criterion, which was developed in 1999, for determining the anomaly ranking of intermediate joint boxes of single-core cables during the maintenance of oil-filled cable joint boxes. However, it can determine neither the fault location nor aid in the diagnosis of terminal joint boxes. In addition, several joint boxes that are determined as normal using this criterion have recently been found to be anomalous in the overhaul. In this paper, we propose a new relatively accurate diagnostic criterion that covers both the intermediate and terminal joint boxes and aids in determining the anomaly ranking and fault location using the multiclass ν-linear support kernel machine (SKM), which we propose as an extension of the linear support vector machine (SVM). The proposed multiclass ν-linear SKM automatically scales data to maximize the performance of the linear SVM and obtains simpler linear evaluation functions. Furthermore, it is formulated as a linear programming problem, whereas general SKMs are formulated as semi-definite programming problems that are difficult to solve. The accuracy of our proposed linear criterion, which was estimated using 5 fold cross-validation, was approximately 75% which was almost comparable to 76% by the one-against-one non-linear RBF-support vector machine.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.134 No.8 （2014） 特集：産業界における新たな通信・ネットワーク技術の活用

本誌掲載ページ: 1138-1147 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/134/8/134_1138/_article/-char/ja/