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

Content Provider	IEEE Xplore Digital Library
Author	Hongbiao Li Lixin Yin
Copyright Year	2010
Description	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.
Starting Page	3485
Ending Page	3488
File Size	449000
Page Count	4
File Format	PDF
ISBN	9781424468805
e-ISBN	9781424468812
DOI	10.1109/iCECE.2010.848
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Excitation control Turbine control Adaptation model Power system dynamics Load model-ing Artificial neural networks DH-HEMTs Reinforcement learning Stability analysis Load modeling
Content Type	Text
Resource Type	Article

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