Abstract
Use of a mixed model consisting of a radial basis function (RBF) network and pole-shifting (PS) feedback controller for power system stabilizer (PSS) application is presented. The RBF network is used for identifying the time-varying parameters of the power system. The RBF has a simple structure with a nonlinear hidden layer which constructs local approximations to nonlinear input-output mapping and a linear output layer. The network is capable of fast learning and represents a nonlinear autoregressive moving average model with exogeneous inputs (NARMAX). The NARMAX model is transformed into a linear model (ARMA) at every sampling instant and the PS controller is used to calculate the control signal. This process of linearizing nonlinear system is important because of widespread industrial acceptance of linear feedback controllers, availability of theoretical and practical results about robustness and closed-loop stability. Simulation studies carried out on a single-machine infinite bus power system verify the effectiveness of the above approach.
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