Online identification of a synchronous machine using a radial basis function network

Author

Abido, M.A. ; Abdel-Magid, Y.L.

Author_Institution

Dept. of Electr. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia

Volume

3

fYear

1997

fDate

4-6 Jun 1997

Firstpage

1946

Abstract

Online identification of a synchronous machine using a radial basis function network (RBFN) is presented. The capability of the proposed identifier to capture the nonlinear operating characteristics of synchronous machines is illustrated. A recursive learning algorithm has been developed to update the network parameters. The results of the proposed identifier performance due to random variations in machine inputs are compared to that obtained by time-domain simulations. Correlation-based model validity tests have been carried out to examine the validity of the proposed identifier. The results demonstrate the adequacy and validity of the proposed RBFN identifier

Keywords

feedforward neural nets; identification; synchronous machines; correlation-based model validity tests; nonlinear operating characteristics; online identification; radial basis function network; random variations; recursive learning algorithm; synchronous machine; time-domain simulations; Feedforward neural networks; Neural networks; Power system analysis computing; Power system control; Power system modeling; Power system simulation; Power system stability; Radial basis function networks; Synchronous machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.611027

Filename

611027