NDLI: An experimental assessment of a stator current MRAS based on neural networks for sensorless control of induction machines

Content Provider	IEEE Xplore Digital Library
Author	Gadoue, S.M. Giaouris, D. Finch, J.W.
Copyright Year	2011
Description	Author affiliation: School of Electric Electronic and Computer Engineering, Newcastle University, U.K. (Gadoue, S.M.; Giaouris, D.; Finch, J.W.)
Abstract	In this paper an experimental evaluation of a novel Model Reference Adaptive System (MRAS) speed observer for induction motor drives based on stator currents is presented. In this scheme the measured stator currents are used as the reference model for the MRAS observer to avoid the use of a pure integrator. A two-layer Neural Network (NN) stator current observer is used as the adaptive model which requires the rotor flux information that can be obtained from the current model. Speed estimation performance of the new MRAS scheme is studied and compared with the classical rotor flux MRAS when applied to an indirect vector control induction motor drive. Experimental results are shown for the two schemes in the low speed region of operation including tests for the regenerating mode. These results complement the simulation results presented for the proposed scheme in a recent work.
Starting Page	102
Ending Page	106
File Size	317784
Page Count	5
File Format	PDF
ISBN	9781457718557
e-ISBN	9781457718540
e-ISBN	9781457718533
DOI	10.1109/SLED.2011.6051552
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-01
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Induction motors MRAS Rotors Stators Observers Neural Networks Induction Machines Mathematical model Sensorless Control
Content Type	Text
Resource Type	Article

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