Operating-condition-dependent ARMA model for PSS application

Author

Zhao, P. ; Malik, O.P.

fYear

2004

fDate

10-10 June 2004

Firstpage

1749

Abstract

Adaptive power system stabilizers (APSS) have attracted plenty of interests in recent years. Most APSSs are model-based. The widely used models in APSSs are auto regression moving average (ARMA) and nonlinear auto regression moving average with exogeneous inputs (NARMAX) models. In This work, an operating-condition-dependent (OC-dependent) ARMA model is presented and realized by local model networks (LMN). The proposed model has the capability of fast learning similar to the RBF-based NARMAX model and can work for various operating conditions without updating its parameters. The effectiveness of the model is verified by simulation studies.

Keywords

autoregressive moving average processes; model reference adaptive control systems; nonlinear systems; power system stability; radial basis function networks; ARMA; LMN; NARMAX; RBF; adaptive power system stabilizers; auto regression moving average; exogeneous inputs; local model networks; model-based APSS; nonlinear auto regression moving average; radial basis function; simulation studies; Adaptive control; Damping; Feedback; Power generation; Power system modeling; Power system simulation; Predictive models; Resonance light scattering; Signal generators; Synchronous generators;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering Society General Meeting, 2004. IEEE

Conference_Location

Denver, CO

Print_ISBN

0-7803-8465-2

Type

conf

DOI

10.1109/PES.2004.1373177

Filename

1373177