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Intelligent Optimal Control of Excitation and Turbine Systems in Power Networks
Abstract
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- text
- 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
- Electrical and Computer Engineering
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Last time updated on 17/10/2019
This paper was published in Missouri University of Science and Technology (Missouri S&T): Scholars' Mine.
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