DocumentCode
1535132
Title
An Online Simplified Rotor Resistance Estimator for Induction Motors
Author
Kenné, Godpromesse ; Simo, Rostand Sorel ; Lamnabhi-Lagarrigue, Françoise ; Arzandé, Amir ; Vannier, Jean Claude
Author_Institution
Dept. de Genie Electr., Univ. de Dschang, Bandjoun, France
Volume
18
Issue
5
fYear
2010
Firstpage
1188
Lastpage
1194
Abstract
This brief presents an adaptive variable structure identifier that provides finite time convergent estimate of the induction motor rotor resistance under feasible persistent of excitation condition. The proposed rotor resistance scheme is based on the standard dynamic model of induction motor expressed in a fixed reference frame attached to the stator. The available variables are the rotor speed, the stator currents and voltages. Experiments show that the proposed method achieved very good estimation of the rotor resistance which is subjected to online large variation during operation of the induction motor. Also, the proposed online simplified rotor resistance estimator is robust with respect to the variation of the stator resistance, measurement noise, modeling errors, discretization effects and parameter uncertainties. Important advantages of the proposed algorithm include that it is an online method (the value of Rr can be continuously updated) and it is very simple to implement in real-time (this feature distinguishes the proposed identifier from the known ones).
Keywords
induction motors; machine control; rotors; stators; variable structure systems; adaptive variable structure identifier; dynamic model; excitation condition; induction motors; noise measurement; online simplified rotor resistance estimator; stator resistance; Electrical resistance measurement; Induction motors; Noise measurement; Noise robustness; Parameter estimation; Rotors; Stators; Testing; Uncertain systems; Voltage; Equivalent injection term; nonlinear observer; online parameter estimation;
fLanguage
English
Journal_Title
Control Systems Technology, IEEE Transactions on
Publisher
ieee
ISSN
1063-6536
Type
jour
DOI
10.1109/TCST.2009.2033790
Filename
5308220
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