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Coherent grouping of power systems for use in training artificial neural networks

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2 Author(s)
McFarlane, A.S. ; Power Res. Lab., McMaster Univ., Hamilton, Ont., Canada ; Alden, R.T.H.

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

Published in:

Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on

Date of Conference:

16-18 Aug 1993