Application of affine NARMA model to design of adaptive power system stabilizer

• Autorzy: Zhou J. , Ke D. , Chung C. Y. , Sun Y.

Identyfikatory

DOI

10.29354/diag/90281

• Języki publikacji: EN

Abstrakty

EN

An affine nonlinear autoregressive moving average (NARMA) model is derived from the neural network (NN) based general NARMA model in this paper, by using Taylor series expansion. The predictive error of this affine NARMA model will be quite acceptable, at least for the control purpose, if the amplitude of control input is properly limited. Therefore, an adaptive control scheme based on this model is proposed and applied to the design of adaptive power system stabilizer (APSS) since the amplitude of PSS output is usually well limited. The feature of this control scheme is that the control input can be online analytically obtained. Thus, comparing to the traditional NN based APSS (TAPSS), the affine NARMA model based APSS (AAPSS) does not need the training of a NN as neuro-controller, which may be a troublesome and time consuming step during the design. Moreover, the AAPSS can generally perform better than the TAPSS. Simulation studies on a single machine infinite bus system and a multi-machine system show that the AAPSSs can consistently well perform to damp electromechanical oscillations in the systems over a wide range of operating conditions.

Słowa kluczowe

EN

adaptive control; affine; NARMA model; neural network; power system stabilizer; PSS

PL

sterowanie adaptacyjne; sieć neuronowa; stabilizator systemu zasilania

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2018
• Tom: Vol. 19, No. 2
• Strony: 105--114
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Zhou J.

• School of Electrical Engineering, Wuhan University, Wuhan, Hubei Province, China, 430072

autor

Ke D.

• School of Electrical Engineering, Wuhan University, Wuhan, Hubei Province, China, 430072

autor

Chung C. Y.

• Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Kanada

autor

Sun Y.

• School of Electrical Engineering, Wuhan University, Wuhan, Hubei Province, China, 430072

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).