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

13

Issue

1

fYear

1998

fDate

2/1/1998 12:00:00 AM

Firstpage

177

Lastpage

183

Abstract

The application of a radial basis function (RBF) neural network (NN) for fault diagnosis in an HVDC power 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; expert systems; fault diagnosis; feedforward neural nets; power system analysis computing; software packages; EMTP package; HVDC power system; HVDC system; adaptive filter; computer simulation; expert knowledge base; fault diagnosis; radial basis function neural network; signal pre-classifier; Adaptive filters; Application software; EMTP; Fault diagnosis; HVDC transmission; Intelligent networks; Neural networks; Packaging; Pattern recognition; Signal processing;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.651633

Filename

651633