Synchronous Generator Control Using Neural Network Based Nonlinear Adaptive Regulator

Abstract

Control equipment of synchronous generators such as automatic voltage regulator, speed governor and power system stabilizer have been developed in order to maintain stability and to improve damping of power systems. When an operating condition changes widely, however, such controllers may become less effective because of nonlinearity of the power system.
In this paper, a nonlinear adaptive generator control system using neural networks is proposed. The proposed neuro-control system consists of two neural networks which work as an identifier and a controller respectively, and generates supplementary control signals to the conventional controllers. An essential feature of the proposed system is that the internal connection weights of both neural networks are adjusted adaptively so as to generate appropriate control signals for transient stability and damping enhancement in response to changes of the operating conditions and the network configuration.
In order to investigate the control performance of the proposed neuro-control system, digital time simula-tions are carried out for a one-machine infinite bus model system. As a result, it is made clear that the proposed adaptive neuro-control system effectively improves the system damping and shows adaptability against the wide changes of the operating conditions.