Copyright © IFAC Power Plants and Power Systems Control, Seoul. Korea. 2003 ELSEVIER IFAC PUBLICATIONS www.elsevier.comllocale/ifac COMPARISON OF GENERALIZED NEURON BASED PSS AND ADAPTIVE PSS D.K. Chaturvedi' O.P. Malik 2 IFacuity of Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra-5, India (e-mail: dkcJoe@redifJmail.com). 2Department of Electrical and Computer Engineering, University of Calgary, 2500, University Drive, N. w., Calgary,AB, Canada, T2N I N4, (e-mail.·malik@enel.llcalgan... ca). Abstract: Artificial neural networks (ANN) can be used as intelligent controllers to control non-linear, dynamic systems through learning, which can easily accommodate the non- linearities and time dependencies. However, they require large training time and large number of neurons to deal with comp)ex problems, Taking benefit of the characteristics of a Generalized Neuron (GN), that requires much smaller training data and shorter training time, a generalized neuron based PSS (GNPSS) and adaptive PSS (GNAPSS) are developed and compared. Copyright ©2003 IFAC Keywords: adaptive PSS, Neural Network, on-line training, Generalized Neuron Controller. I. INTRODUCfION Use of a supplementary control signal in the excitation system and/or the govemor system of a generating unit can provide extra damping for the system and thus improve the unit's dynamic performance (Demello and Laskowski 1979). Power System Stabilizers (PSSs) aid in maintaining power system stability and in improving dynamic performance by providing a supplementary signal to the excitation system. This is an easy, economical and flexible way to improve power system stability. Over the past few decades, PSSs have been extensively studied and successfully used in the industry. The commonly used PSS (CPSS) was first proposed in the 1950s based on a linear model of the power system at some operating point to damp the low frequency oscillations in the system. Linear control theory was employed as the design tool for the CPSS. After decades of theoretical studies and field experiments, this type of PSS has made a great contribution in enhancing the operating quality of the power system (Demello et al. 1978, Larsen and Swan 1981 ). With the development of power systems and increasing demand for quality electricity, it is worthwhile looking into the possibility of using modem control techniques. The linear optimal control strategy is one possibility that has been proposed for supplementary excitation controllers (Ohtsuka et al. 1986). 471 Preciseness of the linear model to represent the actual system and the measurement of some variables are major obstacles to the application of the optimal controller in practice. A more reasonable design of the PSS is based on the adaptive control theory as it takes into consideration the non-linear and stochastic characteristics of the power system (Pierre 1987, Zhang et aI., 1993). This type of stabilizer can adjust its parameters on-line according to the operating condition. Many years of intensive studies have shown that the adaptive stabilizer can not only provide good damping over a wide operating range but more importantly, also can solve the coordination problem among stabilizers. Power systems being dynamic systems, the response time of the controller is the key to a good closed loop performance. Many adaptive control algorithms have been proposed in the recent years. Generally speaking, the better the closed loop system performance is desired, the more complicated the control algorithm becomes, thus needing more on-line computation time to calculate the control signal. More recently, ANNs and fuzzy set theoretic approach have been proposed for power system stabilization problems. A number of papers have been published in the last decade. An illustrative list is given in (Abido et al 1999; Changroon et al. 2000; Hiyama and Lim 1989; Husseinzadeh and kalam 1999; Segal et al. 2000; Swidenbank etal 1999; Yaun 1991). Both techniques have their own advantages and disadvantage. The integration of these approaches can give improved results. The commonly used neuron model has been modified to obtain a generalized neuron (GN) model using fuzzy