A self-tuning power system stabilizer based on artificial neural network

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Date
2004
Authors
Segal, Ravi
Sharma, Avdhesh
Kothari, M L
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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.
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Keywords
Power system stabilizer, Artificial neural network, Small signal stability, Self-tuning controllers
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