Neural network approach to power transmission line fault classification

Author

Wang, Xiao-Ru ; Wu, Si-Tao ; Qian, Qing-Quan

Author_Institution

Sch. of Electr. Eng., Southwest Jiaotong Univ., Sichuan, China

Volume

3

fYear

1997

fDate

9-12 Sep 1997

Firstpage

1737

Abstract

This paper presents a new solution to fault classification of high voltage transmission lines and shows its effectiveness in digital simulation on a realistic 500 kV power system. The scheme is based on backpropagation and Kohonen neural networks and a comparison between them is made. The Electromagnetic Transients Program (EMTP) is used to obtain fault patterns for the training and testing of neural networks. Feature selection, feature extraction and signal procession are studied and a fast, reliable fault classifier is obtained

Keywords

backpropagation; fault diagnosis; feature extraction; pattern classification; power engineering computing; power transmission lines; self-organising feature maps; 500 kV; EMTP; Electromagnetic Transients Program; Kohonen neural networks; backpropagation; digital simulation; fault classification; fault classifier; fault patterns; feature extraction; feature selection; high voltage transmission line; neural network approach; power transmission line; Digital simulation; EMTP; Feature extraction; Neural networks; Power system faults; Power system reliability; Power system simulation; Power system transients; Power transmission lines; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on

Print_ISBN

0-7803-3676-3

Type

conf

DOI

10.1109/ICICS.1997.652293

Filename

652293