Title :
New method for generators´ angles and angular velocities prediction for transient stability assessment of multimachine power systems using recurrent artificial neural network
Author :
Bahbah, Amr G. ; Girgis, Adly A.
Author_Institution :
Clemson Univ., SC, USA
fDate :
5/1/2004 12:00:00 AM
Abstract :
Recurrent radial basis function (RBF) and multilayer perceptron (MLP) artificial neural network (ANN) schemes are proposed for dynamic system modeling, and generators´ angles and angular velocities prediction for transient stability assessment. The method is presented for multimachine power systems. In this scheme, transient stability is assessed based on monitoring generators´ angles and angular velocities with time, and checking whether they exceed the specified limits for system stability or not. Data generation schemes have been proposed. The proposed recurrent ANN scheme is not sensitive to fault locations. It is only dependent on the postfault system configuration.
Keywords :
angular velocity; multilayer perceptrons; power engineering computing; power system transient stability; radial basis function networks; angular velocity prediction; data generation schemes; generator angles; multilayer perceptrons; multimachine power system; recurrent artificial neural network; recurrent radial basis function; transient stability assessment; Angular velocity; Artificial neural networks; Monitoring; Multilayer perceptrons; Power generation; Power system dynamics; Power system faults; Power system modeling; Power system stability; Power system transients;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2004.826765