A Dynamic Model for a Gas-Liquid Corona Discharge Using Neural Networks

Author

Hosny, Ahmed A. ; Hopkins, D.C. ; Gay, Zackery B. ; Safiuddin, Mohammed

Author_Institution

Dept. of Electr. Eng., SUNY - Univ. at Buffalo, Buffalo, NY

Volume

24

Issue

3

fYear

2009

fDate

7/1/2009 12:00:00 AM

Firstpage

1234

Lastpage

1239

Abstract

This paper presents a novel dynamic nonlinear model for pulsed corona discharge using backpropagation neural networks. The Levenberg-Marquardt training algorithm, which is perfectly suitable for fitting functions, is employed. The developed model is based on the voltage-current characteristics of an actual hybrid-series reactor and takes the practical constrains associated with a real system into account. The validity and accuracy of the model have been tested in the Electromagnetic Transients Program, using MODELS language and a TACS-91 time-variant controlled resistor. The results clearly demonstrate that the BPNN-based model is very robust and effective in emulating the chaotic performance for pulsed corona discharge using backpropagation neural networks.

Keywords

EMTP; backpropagation; chaos; corona; languages; power engineering computing; reactors (electric); BPNN-based model; Electromagnetic Transients Program; Levenberg-Marquardt training algorithm; MODELS language; TACS-91 time-variant controlled resistor; backpropagation neural networks; chaotic performance; dynamic nonlinear model; fitting functions; gas-liquid corona discharge; hybrid-series reactor; pulsed corona discharge; voltage-current characteristics; Electromagnetic Transients Program (EMTP); neural networks; pulsed corona discharge;

fLanguage

English

Journal_Title

Power Delivery, IEEE Transactions on

Publisher

ieee

ISSN

0885-8977

Type

jour

DOI

10.1109/TPWRD.2008.2005880

Filename

4914757