DocumentCode :
2540395
Title :
Application of Lyapunov-based adaptive neural network upfc damping controllers for transient stability enhancement
Author :
Chu, Chia-Chi ; Tsai, Hung-Chi
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Tsing Hua Univ., Hsinchu
fYear :
2008
fDate :
20-24 July 2008
Firstpage :
1
Lastpage :
6
Abstract :
A Lyapunov-based adaptive neural network unified power flow controller (UPFC) is developed for improving transient stability of power systems. A simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics and control parameters. The corresponding energy function and the damping control strategy of a classical generator embedded with a UPFC is derived analytically. This energy-based damping control strategy can also be extended into the interconnected power systems by considering the associated two-machine equivalent model. In order to consider more detailed power system models and model uncertainty issues, we incorporate the adaptive recurrent neural network into our UPFC damping controller. This controller can be treated as neural network approximations of Lyapunov control actions in real time and can adjust the corresponding weights in the neural network by the built-in back propagation algorithm. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.
Keywords :
Lyapunov methods; backpropagation; controllability; load flow control; neurocontrollers; power system transient stability; power transformers; Lyapunov control actions; Lyapunov-based adaptive neural network; UPFC damping controllers; adaptive recurrent neural network; built-in backpropagation algorithm; complex transformer; damping control strategy; energy functions; power swing; power system interconnection; power system transient stability; shunt susceptance controllability; two-machine equivalent model; unified power flow controller; Adaptive control; Adaptive systems; Damping; Neural networks; Power system analysis computing; Power system interconnection; Power system modeling; Power system stability; Power system transients; Programmable control; Back Propagation; Energy functions; FACTS; Recurrent Neural network; Transient Stability; UPFC;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE
Conference_Location :
Pittsburgh, PA
ISSN :
1932-5517
Print_ISBN :
978-1-4244-1905-0
Electronic_ISBN :
1932-5517
Type :
conf
DOI :
10.1109/PES.2008.4596580
Filename :
4596580
Link To Document :
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