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

Zhou J., Ke D., Chung C. Y., Sun Y.

Diagnostyka

2018

Vol. 19, No. 2

105--114

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.

Application of a nonlinear hybrid controller in multi-machine power system based on a power system stabilizer

Ahmadi-Zebarjad M., Shahgholian G.

Journal of Power Technologies

2017

Vol. 97, nr 4

295--301

The power system stabilizer (PSS) is used to generate supplementary control signals for the excitation system in order to dampen low frequency oscillations. This paper presents an approach to designing a hybrid power system stabilizer (HPSS), which is a parallel combination of a conventional PSS and a nonlinear control system, to enhance transient stability for multimachine power systems. The effectiveness and applicability of the proposed approach are examined using a standard multimachine power system. The nonlinear system simulation results show that the HPSS is more effective than the conventional PSS in damping oscillations.