Title :
Coherent grouping of power systems for use in training artificial neural networks
Author :
McFarlane, A.S. ; Alden, R.T.H.
Author_Institution :
Power Res. Lab., McMaster Univ., Hamilton, Ont., Canada
Abstract :
This paper presents a methodology for applying artificial neural networks to power systems of various sizes while addressing the problem of increasing training set size with increasing power system size. A slow-coherency based network partitioning technique is used to group the generators and load buses of the 10-machine, 39-bus system into coherent areas. Next we use characteristic parameters of each area as input features to train and perform estimations using a feed-forward neural network
Keywords :
backpropagation; feedforward neural nets; pattern classification; power system analysis computing; artificial neural networks; characteristic parameters; estimations; feedforward neural network; generator grouping; load bus grouping; power system size; slow-coherency based network partitioning; training set size; Analytical models; Artificial neural networks; Circuit faults; Circuit simulation; Feature extraction; Intelligent networks; Power generation; Power system simulation; Power system transients; Power systems;
Conference_Titel :
Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-1760-2
DOI :
10.1109/MWSCAS.1993.342949
