Abstract

Power system is subjected to wide range of operating conditions. Heavily loaded power systems are subjected to Hopf bifurcations resulting in oscillatory instability. A power system shifts to the dynamic instability region encountered by unstable limit cycles, this result in increase of oscillatory behaviour of power system. Many of the authors suggested the theories like Poincare-Bendixson theory, bifurcation theory, eigenvalue theory and shooting methods for finding dynamic stability margin. Small signal stability focuses on eigenvalue computation at a given operating point. The onset of instability is obtained by finding Hopf bifurcations associated with a pair of imaginary eigenvalues using eigenvalue analysis. Computation of these HBs is a tedious and complex task in traditional approach. In this paper, a novel computational algorithm is proposed to identify the loading conditions resulting in these limit cycles, which are associated with a pair of imaginary eigenvalues, using genetic algorithm for single machine infinite bus (SMIB) and multi machine power system (MMPS) models. The derived loading conditions are used to systematically design a robust type-2 fuzzy logic power system stabiliser. The efficacy of the proposed controller is tested for linear and nonlinear models of power system under various operating conditions.

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