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. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances.
G. K. Venayagamoorthy and R. G. Harley, "Intelligent Optimal Control of Excitation and Turbine Systems in Power Networks," Proceedings of the IEEE Power Engineering Society General Meeting, 2006, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at https://doi.org/10.1109/PES.2006.1709491
IEEE Power Engineering Society General Meeting, 2006
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Critic Designs; Approximate Dynamic Programming; Excitation Control; Neural Networks; PI Controllers; Reinforcement Learning; Turbine Control; Adaptive Control; Distribution Networks; Excitation Systems; Intelligent Control; Intelligent Optimal Control; Neurocontrollers; Optimal Control; Optimal Neurocontrol Approaches; Power Grid Highlights; Power Grids; Power Networks; Power System Control; Power System Excitation Control; Power System Stability; Real-Time Laboratory Experimental Studies; System Stabilization; Transmission Networks; Turbine Systems; Turbines; Voltage Control
Article - Conference proceedings
© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2006