APPLICABILITY AND SUITABILITY OF RADIAL BASIS FUNCTION NEURAL NETWORK IN EXCITATION CONTROL SYSTEM OF SYNCHRONOUS MACHINE

The supply of stable, reliable and economic electric energy is a major determinant of industrial progress and consequent rise in the standard of living the world over. The high gain and fast action of excitation system produces the negative damping torque to the rotor of the synchronous generator, which is handled with the introduction of power system stabilizer (PSS). The PSSs mostly discussed/proposed in literature are useful for specified fixed operating conditions. The varying load conditions are a challenge for stability of power system operation. The demand of power system stability is increasing along with the popularity of electrical products. Therefore, variant PSS is required, which should possess self-learning and adaptation properties of handling the changes and uncertainties in the system. To solve this problem radial basis function neural network (RBFNN) based PSS with single machine connected at infinite bus (SMIB) model is proposed by taking angular frequency as an input to improve the transient and dynamic stability of electrical power system at varying loads. The simulations results using Matlab/Simulink and neural network toolbox are compared with conventional and proposed RBFNN PSS at varying load conditions. The applicability and suitability of the proposed PSS show the improvements in transient and dynamic state stability enhancement.

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