Neuro-fuzzy based power system stabilizers for damping oscillations in multi-machine power systems

A very important matter of discussion in power system operation is the oscillation damping problem. Power System Stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. This article describes the design procedure for a fuzzy logic based PSS (FLPSS) and a self-learning adaptive network based fuzzy inference system (ANFIS) type PSS (ANFPSS) which provides supplementary signals thus extending the power stability limits. Speed deviation of a synchronous machine and its derivative are chosen as the input signals to the fuzzy logic controller. The proposed technique has the features of a simple structure, adaptivity and fast response and is evaluated on a multi-machine power system under different operating conditions to demonstrate its effectiveness and robustness. The effect of detuning of one of the generator parameters on the dynamic performance of the system is also analyzed.