Faults Classification for Transmission Line using Wavelet and Radial Base Function

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: 2012/12/01

タイトル(英語): Faults Classification for Transmission Line using Wavelet and Radial Base Function

著者名: Salma Abdel-Aal Shaaban (Dept. of Electrical Engineering & Computer Science Kumamoto University/High Institute of Energy, South Valley University), Takashi Hiyama (Dept. of Electrical Engineering & Computer Science Kumamoto University)

著者名(英語): Salma Abdel-Aal Shaaban (Dept. of Electrical Engineering & Computer Science Kumamoto University/High Institute of Energy, South Valley University), Takashi Hiyama (Dept. of Electrical Engineering & Computer Science Kumamoto University)

キーワード: transmission line faults，wavelets transform，radial base function neural network

要約(英語): This paper presents a framework for fault classification using discrete wavelet transform (DWT) to prepare the feature which would be given as the input to the radial basis function neural network (RBFNN) which has high performance level especially in classification problems. The simulated network carried out using MATLAB/SIMULINK software package for three phase transmission lines which including the series compensator as it is very challenging task in line protection and other online decisions using different ten fault types, different locations along the transmission line and with different fault inception angle.Discrete wavelet energy calculated from quarter cycle of only sending post fault current signals side is used as an input to single RBFNN which is trained and tested to provide all fault types. The method responses very fast with few numbers of training samples and can also detect the ground fault cases as good as other phases without any additional data.

本誌: 電気学会論文誌B（電力・エネルギー部門誌） Vol.132 No.12 （2012）

本誌掲載ページ: 936-941 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejpes/132/12/132_936/_article/-char/ja/