DocumentCode :
2727328
Title :
Application of intelligent control based on neural networks in power system
Author :
Yang, Shengchun ; Yin, Lixin
Author_Institution :
Sch. of Inf. Eng., Northeast Dianli Univ., Jilin, China
fYear :
2011
fDate :
15-17 July 2011
Firstpage :
348
Lastpage :
351
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.
Keywords :
adaptive control; neurocontrollers; optimal control; power grids; power system control; power system stability; turbines; voltage control; adaptive approach; intelligent control; neural networks; nonlinear dynamic loads; optimal neurocontrol approach; power grid; power system; power system excitation; power system stabilization; turbine control systems; voltage control; Adaptation models; DH-HEMTs; Load modeling; Neural networks; Neurocontrollers; Power system stability; Training; excitation control; load modeling; neural networks; reinforcement learning; stability analysis; turbine control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Engineering and Service Science (ICSESS), 2011 IEEE 2nd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-9699-0
Type :
conf
DOI :
10.1109/ICSESS.2011.5982234
Filename :
5982234
Link To Document :
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