Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor
Birame M'hamed 1, 2  
,   Taibi Djamel 2  
,   Sid Ahmed Bessedik 3  
,   Benkhoris Mohamed-Fouad 4  
 
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1
Ledmased Laboratory, University of Laghouat, 03000, Algeria
2
Department of Electrical Engineering, Kasdi Merbah University, Ouargla, Algeria
3
(LACoSERE) University of Laghouat, 03000, Algeria
4
IREENA, Saint Nazaire, Polytech’Nantes, France
CORRESPONDING AUTHOR
Birame M'hamed   

LEDMASED LABORATORY, UNIVERSITY OF LAGHOUAT, 03000, ALGERIA
Submission date: 2020-07-06
Final revision date: 2020-11-04
Acceptance date: 2020-11-07
Online publication date: 2020-11-09
Publication date: 2020-11-09
 
 
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ABSTRACT
Various approaches have been proposed to monitor the state of machines by intelligent techniques such as the neural network, fuzzy logic, neuro-fuzzy, pattern recognition. However, the use of LS-SVM. This article presents an automatic computerized system for the diagnosis and the monitoring of faults between turns of the stator in MI applying the LS-SVM least square support vector machine. in this study for the detection of short circuit faults in the stator winding of the induction motor. Since it requires a mathematical model suitable for modelling defects, a defective IM model is presented. The proposed method uses the stator current as input and at the output decides the state of the motor, indicating the severity of the short-circuit fault.
 
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