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

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dc.contributor.authorKothari, ML-
dc.contributor.authorSegal, R-
dc.contributor.authorGhodki, BK-
dc.date.accessioned2005-05-16T09:42:38Z-
dc.date.available2005-05-16T09:42:38Z-
dc.date.issued1996-01-
dc.identifier.citationIEEE Proceedings of the Power Electronics, Drives and Energy Systems for Industrial Growth, Vol. 2, p.1072 - 1077en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/162-
dc.description.abstractThis 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.en
dc.format.extent365321 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherIEEEen
dc.subjectbackpropagationen
dc.subjectneural netsen
dc.subjectpower engineering computingen
dc.subjectpower system stabilityen
dc.subjectreactive poweren
dc.titleAdaptive conventional power system stabilizer based on artificial neural networken
dc.typeBook chapteren
Appears in Collections:Electrical Engineering

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