DocumentCode :
1879779
Title :
Transient stability and critical clearing time classification using neural networks
Author :
Sharaf, Adel M. ; Lie, T.T. ; Gooi, H.B.
Author_Institution :
Dept. of Electr. Eng., New Brunswick Univ., Fredericton, NB, Canada
fYear :
1993
fDate :
7-10 Dec 1993
Firstpage :
365
Abstract :
The paper presents a novel AI based artificial neural network (ANN) classifier for AC interconnected power system on-line dynamic first swing stability assessment and classification of critical clearing time as either short (below 50 ms) or long from (50 ms-200 ms). The classification is done using a multi-layer logsigmoid activation and back error propagation (BEP) for training and weight adjustment. The detection scheme is based on a discriminant vector of FFT spectra of synchronous generator rotor angle, speed deviations, and accelerating power magnitude and their cross correlations power spectra
Keywords :
backpropagation; fast Fourier transforms; neural nets; power system analysis computing; power system stability; power system transients; synchronous generators; AC interconnected power systems; FFT spectra; accelerating power magnitude; artificial neural network; back error propagation; critical clearing time classification; cross correlations power spectra; detection scheme; discriminant vector; multi-layer logsigmoid activation; on-line dynamic first swing stability assessment; rotor angle; speed deviations; synchronous generators; training; transient stability classification; weight adjustment;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Advances in Power System Control, Operation and Management, 1993. APSCOM-93., 2nd International Conference on
Conference_Location :
IET
Print_ISBN :
0-85296-569-9
Type :
conf
Filename :
292740
Link To Document :
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