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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/162

Title: Adaptive conventional power system stabilizer based on artificial neural network
Authors: Kothari, ML
Segal, R
Ghodki, BK
Keywords: backpropagation
neural nets
power engineering computing
power system stability
reactive power
Issue Date: Jan-1996
Publisher: IEEE
Citation: IEEE Proceedings of the Power Electronics, Drives and Energy Systems for Industrial Growth, Vol. 2, p.1072 - 1077
Abstract: This paper deals with an artificial neural network (ANN) based adaptive conventional power system stabilizer (PSS). The ANN comprises an input layer, a hidden layer and an output layer. The input vector to the ANN comprises real power (P) and reactive power (Q), while the output vector comprises optimum PSS parameters. A systematic approach for generating training set covering a wide range of operating conditions is presented. The ANN has been trained using a back-propagation training algorithm. Investigations reveal that the dynamic performance of ANN based adaptive conventional PSS is quite insensitive to wide variations in loading conditions.
URI: http://eprint.iitd.ac.in/dspace/handle/2074/162
Appears in Collections:Electrical Engineering

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