Adaptive Critic Designs for Optimal Control of Power Systems

Citation data:

Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, Vol: 2005, Page: 136-148

Publication Year:
2005
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Repository URL:
http://scholarsmine.mst.edu/ele_comeng_facwork/1417
DOI:
10.1109/isap.2005.1599253
Author(s):
Venayagamoorthy, Ganesh K.; Harley, Ronald G.
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE)
Tags:
Engineering; Adaptive Control; Adaptive Critic Design; Adaptive Neurocontrol; Approximate Dynamic Programming; Excitation Control; Flexible AC Transmission Systems; Load Flow Control; Neural Network; Neurocontrollers; Optimal Control; Optimal Neurocontrol; Power System Control; Reinforcement Learning; Turbine Control; Voltage Control; Adaptive Control; Adaptive Critic Design; Adaptive Neurocontrol; Approximate Dynamic Programming; Excitation Control; Flexible AC Transmission Systems; Load Flow Control; Neural Network; Neurocontrollers; Optimal Control; Optimal Neurocontrol; Power System Control; Reinforcement Learning; Turbine Control; Voltage Control; Electrical and Computer Engineering
conference paper description
The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and Flexible AC Transmission Systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and real-time laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances. © 2005 ISAP.