Neural network based power system stabilizers

Author

Sharaf, A.M. ; Lie, T.T. ; Gooi, H.B.

Author_Institution

Dept. of Electr. Eng., New Brunswick Univ., Fredericton, NB, Canada

fYear

1993

fDate

24-26 Nov 1993

Firstpage

306

Lastpage

309

Abstract

Novel power system artificial neural network (ANN) based power system stabilizers (PSSs) are presented. The two ANN-PSS designs are driven by the speed error and its rate of change. Other supplementary stabilizing signals such as voltage deviation, excursion error, and PSS output rate of change are utilized to ensure the best matching between the ANN-PSS design and the optimized conventional analog PSS benchmark model. The use of ANN based PSSs is motivated by their noise rejection and robustness under varying network topologies, loading conditions, parametric variations, and model uncertainties

Keywords

feedforward neural nets; intelligent control; power system control; stability; ANN-PSS design; PSS output rate of change; excursion error; f; feedforward neural network; loading conditions; model uncertainties; network topologies; neural network based power system stabilizers; noise rejection; optimized conventional analog PSS benchmark model; parametric variations; rate of change; robustness; speed error; stabilizing signals; voltage deviation; Artificial neural networks; Damping; Network topology; Neural networks; Power system dynamics; Power system interconnection; Power system measurements; Power system modeling; Power system stability; Power systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Neural Networks and Expert Systems, 1993. Proceedings., First New Zealand International Two-Stream Conference on

Conference_Location

Dunedin

Print_ISBN

0-8186-4260-2

Type

conf

DOI

10.1109/ANNES.1993.323018

Filename

323018