Application of affine NARMA model to design of adaptive power system stabilizer
Jingsen Zhou 1  
,   Deping Ke 2  
,   Chi Yong Chung 3  
,   Yuanzhang Sun 1  
More details
Hide details
School of Electrical Engineering, Wuhan University, Wuhan, Hubei Province, China
School of Electrical Engineering, Wuhan University
Department of Electrical and Computer Engineering , University of Saskatchewan, Saskatoon, Saskatchewan, Canada)
Submission date: 2018-02-03
Final revision date: 2018-04-10
Acceptance date: 2018-04-23
Online publication date: 2018-04-26
Publication date: 2018-06-11
Diagnostyka 2018;19(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 multimachine system show that the AAPSSs can consistently well perform to damp electromechanical oscillations in the systems over a wide range of operating conditions.
Chaturvedi DK, Malik OP, Kalra PK. Performance of a generalized neuron-based PSS in a multi-machine power system. IEEE Trans. Energy Convers, 2004; 19(3): 625-632.
Chaturvedi DK, Malik OP. Generalized neuron-based adaptive PSS for multi-machine environment. IEEE Trans Power Syst, 2005; 20(1): 358-366.
Chaturvedi DK, Malik OP. Neurofuzzy power system stabilizer. IEEE Trans Energy Convers, 2008; 23(3): 887-894.
Chung CY, Wang KW, Tse CT, Riu N. PSS design by probabilistic sensitivity indices. IEEE Trans Power Systs, 2002; 17(3): 688-693.
Deng H, Li HX, Wu YH. Feedback-linearization-based neural adaptive control for unknown nonaffine nonlinear discrete-time systems. IEEE Trans Neural Netw, 2008 19(9): 1615-1625.
He J, Malik OP. An adaptive power system stabilizer based on recurrent neural networks. IEEE Trans Energy Convers, 1997; 12(4): 413-418. 10.1109/60.638966.
Kundar P. Power System Stability and Control. New York: McGraw-Hill, 1994.
Mohagheghi S, Venayagamoorthy GK, Harley RG. Optimal wide area controller and state predictor for a power system. IEEE Trans Power Syst, 2007; 22(2): 693-705.
Narendra KS, Mukhopadhyay S. Adaptive control using neural networks and approximate models. IEEE Trans Neural Netw, 1997; 8(3): 475-485.
Nguyen TT, Gianto R. Neural networks for adaptive control coordination of PSSs and FACTS devices in multi-machine power system. IET Gener Transm Distrib, 2007; 2(3): 355-372.
Park JW, Venayagamoorthy GK, Harley RG. MLP/RBF neural-networks-based online global model identification of synchronous generator. IEEE Trans Ind Electron, 2005; 52(6): 1685-1695.
Park YM, Moon UC, Lee KY. A self-organizing power system stabilizer using fuzzy auto-regressive moving average (FARMA) model. IEEE Trans Energy Convers, 1996; 11(2): 442-448.
Ramakrishna G, Malik OP. Radial basis function identifier and pole-shifting controller for power system stabilizer application. IEEE Trans Energy Convers, 2004; 19(4): 663-670.
Ramirez-Gonzalez M, Malik OP. Power system stabilizer design using an online adaptive neurofuzzy controller with adaptive input link weights. IEEE Trans Energy Convers, 2008; 23(3): 914-922.
Sarangapani J. Neural network control of nonlinear discrete-time systems. Boca Raton: CRC Press; 2006.
Segal R, Kothari ML, Madnani S. Radial basis function (RBF) network adaptive power system stabilizer. IEEE Trans Power Syst, 2000; 15(2): 722-727.
Shamsollahi P, Malik OP. An adaptive power system stabilizer using on-line trained neural networks. IEEE Trans Energy Convers, 1987; 12(4): 382-387.
Wu B, Malik OP. Multivariable adaptive control of synchronous machines in a multi-machine power system. IEEE Trans Power Syst, 2006; 21(4): 1772-1781.
You R, Eghbali HJ, Nehrir MH. An online adaptive neuro-fuzzy power system stabilizer for multi-machine systems. IEEE Trans Power Syst, 2003; 18(1): 128-135.
Zhang Y, Chen GP, Malik OP, Hope GS. An artificial neural network based adaptive power system stabilizer. IEEE Trans. Energy Convers, 1993; 8(1): 71-77.