Application of evolutionary programming to transient and subtransient parameter estimation

Author

Lai, L.L. ; Ma, J.T.

Author_Institution

Dept. of Electr. Electron. & Inf. Eng., City Univ., London, UK

Volume

11

Issue

3

fYear

1996

fDate

9/1/1996 12:00:00 AM

Firstpage

523

Lastpage

530

Abstract

This paper presents an artificial intelligence approach of using evolutionary programming to estimate the transient and subtransient parameters of a generator under normal operation. The estimation using evolutionary programming is compared with that using a corrected extended Kalman filter. The comparisons with both simulation and micromachine test results show that evolutionary programming is robust to search the real values of parameters even when the data are highly contaminated by noise, while with the extended Kalman filter, the estimation tends to diverge with such data

Keywords

Kalman filters; artificial intelligence; electric generators; electric machine analysis computing; parameter estimation; programming; artificial intelligence approach; corrected extended Kalman filter; evolutionary programming; generator; micromachine; subtransient parameter estimation; transient parameter estimation; Artificial intelligence; Genetic programming; Parameter estimation; Power engineering and energy; Power generation; Power system analysis computing; Power system dynamics; Power system faults; Power system stability; Testing;

fLanguage

English

Journal_Title

Energy Conversion, IEEE Transactions on

Publisher

ieee

ISSN

0885-8969

Type

jour

DOI

10.1109/60.537003

Filename

537003