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Low speed operation improvement of MRAS sensorless vector control induction motor drive using neural network flux observers

Lookup NU author(s): Dr Shady Gadoue, Professor Damian Giaouris, Emeritus Professor John Finch


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This paper presents a novel neural network-based flux observer to solve the low speed problems associated with a model reference adaptive speed estimation scheme which is based on rotor flux. A multilayer feedforward artificial neural network is proposed for rotor flux estimation which is more robust to noise and stator resistance variation and does not have dc-drift problems which are usually associated with these adaptive schemes. A comparison between the performance of the neural network based strategy and conventional scheme is carried out using a validated simulation of an indirect vector controlled induction motor drive working at a low speed. © 2006 IEEE.

Publication metadata

Author(s): Gadoue SM, Giaouris D, Finch JW

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 32nd Annual Conference of the IEEE Industrial Electronics Society (IECON 2006)

Year of Conference: 2006

Pages: 1212-1217

ISSN: 1553-572X

Publisher: IEEE


DOI: 10.1109/IECON.2006.347284

Library holdings: Search Newcastle University Library for this item

ISBN: 1424403901