DocumentCode :
2751415
Title :
Rotor Speed Identification of Doubly-Fed Generator System Based on Neural Network
Author :
Li Lan ; Liu Yanli
Author_Institution :
Coll. of Electr. & Power Eng., Taiyuan Univ. of Technol., Taiyuan, China
fYear :
2009
fDate :
5-6 Dec. 2009
Firstpage :
146
Lastpage :
149
Abstract :
According to the mathematic model of doubly-fed induction generator, the adjustable model and reference model based on MRAS are found. The adjustable model of model reference adaptive system based on neural network is derived by backward differentiation method and the algorithm for magnetic flux linkage is determined using two-layer neural network . Besides, the speed identification of doubly-fed induction generator is obtained by training of two-layer neural network using error back propagation. Finally, the simulation results show that compared with the speed identification of model reference adaptive system, the rotor speed can be reflected actually, and the speed estimation precision is effectively improved when neural network speed estimator is applied.
Keywords :
angular velocity control; asynchronous generators; backpropagation; couplings; differentiation; error compensation; machine control; magnetic flux; model reference adaptive control systems; neural nets; rotors; MRAS; backward differentiation method; doubly-fed induction generator system; error back propagation; magnetic flux linkage; mathematic model; model reference adaptive system; rotor speed identification; speed estimation precision; training; two-layer neural network; Adaptive control; Adaptive systems; Artificial neural networks; Couplings; Educational institutions; Induction generators; Mathematical model; Neural networks; Power engineering; Rotors; adjustable model; model reference adaptive system; neural network; speed identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
E-Learning, E-Business, Enterprise Information Systems, and E-Government, 2009. EEEE '09. International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-0-7695-3907-2
Type :
conf
DOI :
10.1109/EEEE.2009.57
Filename :
5359175
Link To Document :
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