Control of Turbine and Excitation in Power System Based on Neural Networks

Abstract

Increasingly nonlinear dynamic loads have been connected into power systems; such as variable speed drives, robotic factories and power electronics loads. This adds to the complexity of load modeling. The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation and turbine systems. The crucial factors affecting the modern power systems today is voltage control and system stabilization during small and large disturbances. Simulation studies and real-time laboratory experimental studies carried out are described and the results show the successful control of the power system excitation and turbine systems with adaptive and optimal neurocontrol approaches.