Fast real power contingency ranking using a counterpropagation network

Author

Lo, K.L. ; Peng, L.J. ; Macqueen, J.F. ; Ekwue, A.O. ; Cheng, D.T.Y.

Author_Institution

Dept. of Electron. & Electr. Eng., Strathclyde Univ., Glasgow, UK

Volume

13

Issue

4

fYear

1998

fDate

11/1/1998 12:00:00 AM

Firstpage

1259

Lastpage

1264

Abstract

This paper proposes a fast real power contingency ranking approach which is based on a pattern recognition technique using a forward-only counterpropagation neural network (CPN). The power system operating state is described by a set of variables which compose the pattern. The corresponding performance indices of various contingencies can then be recognised by a properly trained counterpropagation network. A feature selection method is also employed for reducing the dimensionality of the input patterns. When compared with a full AC load flow the proposed method is more superior and has good pattern recognition ability

Keywords

learning (artificial intelligence); neural nets; pattern recognition; power system analysis computing; power system security; AC load flow; computer simulation; contingency performance indices; feature selection method; forward-only counterpropagation neural network; input patterns dimensionality; neural net training; pattern recognition technique; power system operating state; real power contingency ranking; Artificial neural networks; Load flow; Neural networks; Pattern recognition; Power system analysis computing; Power system economics; Power system measurements; Power system security; Power systems; System testing;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.736256

Filename

736256