Decentralized nonlinear optimal predictive excitation control for multi-machine power systems

Abstract

This paper presents a novel decentralized nonlinear excitation controller based on a nonlinear optimal predictive control theory for multi-machine power systems to enhance their transient stability. A key feature of the proposed excitation controller is that it does not require online optimization and the huge computation burden is avoided. There are only two controller parameters, i.e. prediction horizon and control order, needed to be determined at the design stage. Moreover, as the proposed excitation controller only requires local and direct measurements used as input signals, it can be implemented locally and dispersedly for individual generators and is convenient for industrial applications. Case studies are performed based on a three-machine six-bus power system. Simulation results demonstrate the effectiveness of the proposed nonlinear excitation controller in terms of improving dynamic stability and robust performance under various operating conditions.