Comparative Analysis of Power System Stabilizer under small scale stability considerations using conventional,Neural Network and Fuzzy Logic Based Controllers

Abstract

The low frequency oscillations, which are typically are of the frequency range of 0.2 to 3.0 Hz, are excited by the disturbances in the system or, in some cases, might even build up spontaneously. These oscillations may cause a loss of synchronism and an eventual breakdown of the entire system. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. The traditional solution to this problem is application of conventional power system stabilizer (CPSS). The constantly changing nature of power system makes the design of CPSS a difficult task. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this thesis uses an Artificial Intelligent techniques based Power System Stabilizer design. These AI techniques have features of simple structure, adaptivity and fast response. The model is evaluated on a single machine infinite bus power system, then the performance of CPSS, neural network based PSS and Fuzzy Based PSS is compared. The AI based PSS designs give better performance than the conventional PSS.