NDLI: Training recurrent neural network vector controller for inner current-loop control of doubly fed induction generator

Content Provider	IEEE Xplore Digital Library
Author	Xingang Fu Shuhui Li
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Alabama, Tuscalooa, AL, USA (Xingang Fu; Shuhui Li)
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.
Starting Page	1
Ending Page	5
File Size	399256
Page Count	5
File Format	PDF
ISBN	9781467380409
DOI	10.1109/PESGM.2015.7285980
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Artificial neural networks Rotors Jacobian matrices Stator windings Machine vector control vector control Doubly fed induction generator recurrent neural network Levenberg-Marquardt Forward Accumulation Through Time approximate optimal control
Content Type	Text
Resource Type	Article

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