DocumentCode :
601842
Title :
Fault Tolerance of Stator Turn Fault for Three Phase Induction Motors Star-Connected Using Artificial Neural Network
Author :
Refaat, Shady S. ; Abu-Rub, Haitham ; Saad, M.S. ; Aboul-Zahab, Essam M. ; Iqbal, Atif
Author_Institution :
Texas A&M University at Qatar, Doha, Qatar
fYear :
2013
fDate :
17-21 March 2013
Firstpage :
2336
Lastpage :
2342
Abstract :
This paper proposes the possibility of developing incipient fault diagnosis and remedial operating strategies, which enable a fault tolerant induction motor star-connected winding with neutral point earthed through a controllable impedance using artificial neural network (ANN). The fault detection and diagnosis is achieved by using a strategy that detects stator turn fault, isolates the faulty components, identifies fault severity and reduces the propagation speed of the incipient stator winding fault. The fault tolerance is obtained by controlled neutral grounding resistor. This allows for continuous free operation of the induction motor even with stator winding faults. The advantage of this strategy is that it does not require any change in the standard drive system. Experimental results demonstrate the validity of the proposed technique.
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applied Power Electronics Conference and Exposition (APEC), 2013 Twenty-Eighth Annual IEEE
Conference_Location :
Long Beach, CA, USA
ISSN :
1048-2334
Print_ISBN :
978-1-4673-4354-1
Electronic_ISBN :
1048-2334
Type :
conf
DOI :
10.1109/APEC.2013.6520621
Filename :
6520621
Link To Document :
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