Well-being analysis for composite generation and transmission systems based on pattern recognition techniques

Author

Da Silva, A. M Leite ; de Resende, L.C. ; da Fonseca Manso, L.A. ; Miranda, V.

Author_Institution

Dept. of Electr. Eng., Fed. Univ. of Itajuba, Itajuba

Volume

2

Issue

2

fYear

2008

fDate

3/1/2008 12:00:00 AM

Firstpage

202

Lastpage

208

Abstract

A new methodology to evaluate well-being indices for a composite generation and transmission system, based on non-sequential Monte Carlo simulation and pattern recognition techniques, is presented. To classify the success operating states into healthy and marginal, an artificial neural network based on group method data handling techniques is used to capture the patterns of these state classes, during the beginning of the simulation process. The idea is to provide the simulation process with an intelligent memory, based on polynomial parameters, to speed up the evaluation of the operating states. The proposed methodology is applied to the IEEE reliability test system (IEEE-RTS), to the IEEE-RTS-96 and to a configuration of the Brazilian South-Southeastern system.

Keywords

Monte Carlo methods; neural nets; pattern recognition; power engineering computing; power generation reliability; power transmission reliability; Brazilian South-Southeastern system; IEEE reliability test system; IEEE-RTS-96; artificial neural network; composite generation/transmission systems; group method data handling techniques; intelligent memory; nonsequential Monte Carlo simulation; pattern recognition techniques; polynomial parameters; well-being indices;

fLanguage

English

Journal_Title

Generation, Transmission & Distribution, IET

Publisher

iet

ISSN

1751-8687

Type

jour

DOI

10.1049/iet-gtd:20070109

Filename

4459235