A neural network architecture for static security mapping in power systems

Author

Cirrincione, G. ; Cirrincione, M. ; Piglione, F.

Author_Institution

TIRF-INPG, Grenoble, France

Volume

3

fYear

1996

fDate

13-16 May 1996

Firstpage

1611

Abstract

In this paper a neural network based architecture, which combines supervised and unsupervised learning for the static security assessment of power systems, is presented. The proposed method allows the on-line security evaluation of a possible outage simply by considering the position of the neuron activated by the pre-fault state vector in an output map, allowing an easy and immediate view of the contingency risks. The mapping capabilities of two unsupervised neural networks, SOM (self-organising map) and CCA (curvilinear component analysis), are compared. Numerical tests, carried out on a study system, are presented and discussed

Keywords

power system analysis computing; power system security; self-organising feature maps; unsupervised learning; contingency risks; curvilinear component analysis; neural network architecture; on-line security evaluation; output map; power systems; pre-fault state vector; self organising map; static security assessment; static security mapping; supervised learning; unsupervised learning; Artificial neural networks; Information security; Intelligent networks; Network synthesis; Neural networks; Neurons; Power generation; Power system management; Power system security; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrotechnical Conference, 1996. MELECON '96., 8th Mediterranean

Conference_Location

Bari

Print_ISBN

0-7803-3109-5

Type

conf

DOI

10.1109/MELCON.1996.551261

Filename

551261