Application of a radial basis function (RBF) neural network for fault diagnosis in a HVDC system

Author

Narendra, K.G. ; Sood, V.K. ; Khorasani, K. ; Patel, R.

Author_Institution

Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada

Volume

1

fYear

1996

fDate

8-11 Jan 1996

Firstpage

140

Abstract

The application of a radial basis function (RBF) neural network (NN) for fault diagnosis in a HVDC system is presented in this paper. To provide a reliable pre-processed input to the RBF NN, a new pre-classifier is proposed, This pre-classifier consists of an adaptive filter (to track the proportional values of the fundamental and average components of the sensed system variables), and a signal conditioner which uses an expert knowledge base (KB) to aid the pre-classification of the signal. The proposed method of fault diagnosis is evaluated using simulations performed with the EMTP package

Keywords

HVDC power transmission; adaptive filters; fault diagnosis; fault location; feedforward neural nets; knowledge based systems; power system analysis computing; signal processing; EMTP package; HVDC system; adaptive filter; expert knowledge base; fault diagnosis; pre-classifier; pre-processed input; radial basis function neural network; sensed system variables; signal conditioner; Control systems; Fault diagnosis; HVDC transmission; Intelligent networks; Least squares approximation; Neural networks; Neurons; Power system faults; Signal processing; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics, Drives and Energy Systems for Industrial Growth, 1996., Proceedings of the 1996 International Conference on

Conference_Location

New Delhi

Print_ISBN

0-7803-2795-0

Type

conf

DOI

10.1109/PEDES.1996.537295

Filename

537295