Damping of low frequency oscillations in power system network using swarm intelligence tuned fuzzy controller

In this paper, a particle swarm intelligent optimisation based optimal fuzzy scheme has been developed to design intelligent adaptive controllers for improving the dynamic and transient stability performance of multimachine power system. This concept is applied to power system stabiliser (PSS) connected to a nine bus power system network having three synchronous machines. The rules of the neuro-fuzzy scheme are derived from the speed error and their derivatives. The parameters of the fuzzy logic are to be optimised for better control performance. The optimisation of the fuzzy logic parameters are performed through particle swarm intelligent algorithm. The performance of the proposed controller is analysed in multimachine power systems subjected to various dynamic and transient disturbances. The proposed particle swarm intelligent neuro-fuzzy control scheme exhibits a superior damping performance in comparison to the existing controllers. The advantage of neuro-fuzzy logic and particle swarm optimisation shows that the proposed technique is attractive for real-time implementation. The results evident that such a nonlinear adaptive PSS will yield better and fast damping, under small and large disturbances even with change in system operating conditions.