A fault classification method by RBF neural network with OLS learning procedure

Author

Lin, Whei-Min ; Yang, Chin-Der ; Lin, Jia-Hong ; Tsay, Ming-Tong

Author_Institution

Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan

Volume

16

Issue

4

fYear

2001

fDate

10/1/2001 12:00:00 AM

Firstpage

473

Lastpage

477

Abstract

This paper presents a new approach to identify fault types and phases. A transmission line fault classification method based on a radial basis function (RBF) neural network with orthogonal-least-square (OLS) learning procedure was used to identify various patterns of associated voltages and currents. The RBF neural network was also compared with the back-propagation (BP) neural network in this paper. It is shown that the RBF approach can provide a fast and precise operation for various faults. The simulation results also show that the proposed approach can be used as an effective tool for high speed relaying

Keywords

fault location; least squares approximations; power system analysis computing; power transmission faults; power transmission lines; power transmission protection; radial basis function networks; relay protection; RBF neural network; back-propagation neural network; fault classification method; high speed relaying; orthogonal-least-square learning; radial basis function neural network; transmission line protection; Artificial neural networks; Fault diagnosis; Neural networks; Power system protection; Power system simulation; Protective relaying; Radial basis function networks; Testing; Vectors; Voltage;

fLanguage

English

Journal_Title

Power Delivery, IEEE Transactions on

Publisher

ieee

ISSN

0885-8977

Type

jour

DOI

10.1109/61.956723

Filename

956723