DocumentCode :
2191991
Title :
New method for generators´ angles and angular velocities prediction for transient stability assessment of multi-machine power systems using recurrent artificial neural network
Author :
Bahbah, A. ; Girgis, A.
Author_Institution :
Clemson Univ., SC, USA
fYear :
2004
fDate :
6-10 June 2004
Abstract :
Summary form only given. Recurrent radial basis function (RBF), and multi-layer 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 multi-machine 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 post-fault system configuration.
Keywords :
angular velocity; electric generators; fault location; multilayer perceptrons; power engineering computing; power system faults; power system transient stability; radial basis function networks; angles prediction; angular velocities prediction; data generation scheme; fault location; multilayer perceptron; 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;
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.2004.1372769
Filename :
1372769
Link To Document :
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