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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1409

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DC FieldValueLanguage
dc.contributor.authorSegal, Ravi-
dc.contributor.authorSharma, Avdhesh-
dc.contributor.authorKothari, M L-
dc.date.accessioned2006-02-23T04:11:29Z-
dc.date.available2006-02-23T04:11:29Z-
dc.date.issued2004-
dc.identifier.citationInternational Journal of Electrical Power & Energy Systems, 26(6), 423-430en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/1409-
dc.description.abstractThis paper presents a systematic approach for designing a self-tuning power system stabilizer (PSS) based on artificial neural network (ANN). An ANN is used for self-tuning the parameters of PSS in real-time. The nodes in the input layer of the ANN receive generator terminal active power (P), reactive power (Q), and voltage (Vt), while the nodes in the output layer provide the optimum PSS parameters, e.g. stabilizing gain (KSTAB), time constants (T1 and T2). A new approach for the selection of number of neurons in the hidden layer has been proposed. Investigations reveal that the dynamic performance of the system with self-tuning PSS based on ANN (ST-ANNPSS) is quite robust over a wide range of loading conditions and equivalent reactance, Xe.en
dc.format.extent424969 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.subjectPower system stabilizeren
dc.subjectArtificial neural networken
dc.subjectSmall signal stabilityen
dc.subjectSelf-tuning controllersen
dc.titleA self-tuning power system stabilizer based on artificial neural networken
dc.typeArticleen
Appears in Collections:Electrical Engineering

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