Abstract
AbstractControl equipment of synchronous generators such as automatic voltage regulators, speed governors and power system stabilizers have been developed to maintain stability and to improve damping of power systems. When an operating condition changes greatly, 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 neurocontrol 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.To investigate the control performance of the proposed neurocontrol system, digital time simulations are carried out for a one‐machine infinite bus model system. As a result, it is clarified that the proposed adaptive neurocontrol system effectively improves the system damping and shows adaptability against the wide changes of the operating conditions.
Published Version
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