DocumentCode :
3665528
Title :
Training recurrent neural network vector controller for inner current-loop control of doubly fed induction generator
Author :
Xingang Fu; Shuhui Li
Author_Institution :
Department of Electrical and Computer Engineering, The University of Alabama, Tuscalooa, 35401, USA
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
1
Lastpage :
5
Abstract :
This paper proposes a novel recurrent neural network (RNN) based vector control method for a doubly fed induction generator (DFIG) and especially focuses on how to train the neural network controller for the current-loop control of DFIG. The proposed RNN vector control utilizes the stator voltage oriented frame and the role of the RNN is to substitute the two decoupled current-loop PI controllers in the conventional vector control technique. The objective of RNN training is to approximate optimal control and the RNN controller was trained by Levenberg-Marquardt (LM) algorithm. Forward Accumulation Through Time algorithm for the DFIG was developed to calculate Jacobian matrix needed by LM algorithm. Performance evaluation shows that the well-trained RNN controller has a very strong ability of tracking references under situations such as quickly rapid change reference and rotor parameter change.
Keywords :
"Training","Artificial neural networks","Rotors","Jacobian matrices","Stator windings","Machine vector control"
Publisher :
ieee
Conference_Titel :
Power & Energy Society General Meeting, 2015 IEEE
ISSN :
1932-5517
Type :
conf
DOI :
10.1109/PESGM.2015.7285980
Filename :
7285980
Link To Document :
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