An online trained fuzzy neural network controller to improve stability of power systems

Abstract

The purpose of this paper is to improve the stability in a power system using a new intelligent controller. This controller is an online trained fuzzy neural network controller (OTFNNC) in which adaptive learning rates derived by the Lyapunov stability are employed to guarantee the convergence of the proposed controller. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller. One of the proposed controller features is robustness to different operating conditions and disturbances. Moreover, the Prony method is used to obtain the exponential damping of power system oscillations in this paper.The test power system is a two-area four-machine system power. The simulation results show that the oscillations are satisfactorily damped out by the OTFNNC. The proposed approach is effective to mitigate power system oscillations and improve the stability. Literature review show that no method is proposed to compute the damping of power system oscillation if adaptive and online controllers like fuzzy and neural network controller are utilized for damping power system oscillations. In this paper, the damping rate of power system oscillations is estimated by the Prony method.