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

Title: A self-tuning power system stabilizer based on artificial neural network
Authors: Segal, Ravi
Sharma, Avdhesh
Kothari, M L
Keywords: Power system stabilizer
Artificial neural network
Small signal stability
Self-tuning controllers
Issue Date: 2004
Citation: International Journal of Electrical Power & Energy Systems, 26(6), 423-430
Abstract: This 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.
URI: http://eprint.iitd.ac.in/dspace/handle/2074/1409
Appears in Collections:Electrical Engineering

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