Title :
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
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;
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
DOI :
10.1109/ANNES.1993.323018
