位相分解部分放電パターン再構築とニューラルネットワークによる変電所の絶縁診断

カテゴリ: 論文誌(論文単位)

グループ名: 【A】基礎・材料・共通部門

発行日: 2022/03/01

タイトル(英語): Examination of Insulation Diagnosis in Substation by Neural Network with Phase-resolved Partial Discharge Pattern Reconstruction

著者名: 藤岡　駿弥（九州工業大学），河野　英昭（九州工業大学），小迫　雅裕（九州工業大学），匹田　政幸（九州工業大学），枝　修（東京電設サービス（株）），谷口　修平（東京電設サービス（株）），椎名　康晴（東京電設サービス（株））

著者名(英語): Shunnya Fujioka (Kyushu Institute of Technology), Hideaki Kawano (Kyushu Institute of Technology), Masahiro Kozako (Kyushu Institute of Technology), Masayuki Hikita (Kyushu Institute of Technology), Osamu Eda (Tokyo Densetu Service Co., Ltd.), Shuhei Yaguchi (Tokyo Densetu Service Co., Ltd.), Yasuharu Shiina (Tokyo Densetu Service Co., Ltd.)

キーワード: 変電所，絶縁診断，部分放電，ニューラルネットワーク，位相分解部分放電パターン　　substation，insulation diagnosis，partial discharge，artificial neural network，phase-resolved partial discharge

要約(英語): Several studies for partial discharge (PD) pattern recognition using artificial neural network (ANN) were reported in the early 1990s. Usually, in an actual field such as a substation, data on partial discharge is scarcely available, or even rare. In many cases, the power supply phase required for the PRPD pattern cannot be easily obtained. We propose an ANN method that shifts the phase in which the maximum signal intensity detected with PD sensors is generated and used it as training and input data, even for the few phases resolved PD data available in the field. This ANN method was applied to the PRPD pattern obtained in a practical field. As a result, it was shown that the discrimination rate between PD and noise was improved, and therefore the proposed ANN method was found to be effective.

本誌: 電気学会論文誌A（基礎・材料・共通部門誌） Vol.142 No.3 （2022） 特集：放電・プラズマ・パルスパワー研究の最新動向

本誌掲載ページ: 94-100 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/ieejfms/142/3/142_94/_article/-char/ja/