@article{(Open Science Index):https://publications.waset.org/pdf/4333,
	  title     = {Fault Classification of a Doubly FED Induction Machine Using Neural Network},
	  author    = {A. Ourici},
	  country	= {},
	  institution	= {},
	  abstract     = {Rapid progress in process automation and tightening
quality standards result in a growing demand being placed on fault
detection and diagnostics methods to provide both speed and
reliability of motor quality testing. Doubly fed induction generators
are used mainly for wind energy conversion in MW power plants.
This paper presents a detection of an inter turn stator and an open
phase faults, in a doubly fed induction machine whose stator and
rotor are supplied by two pulse width modulation (PWM) inverters.
The method used in this article to detect these faults, is based on
Park-s Vector Approach, using a neural network.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {3},
	  number    = {6},
	  year      = {2009},
	  pages     = {1373 - 1376},
	  ee        = {https://publications.waset.org/pdf/4333},
	  url   	= {https://publications.waset.org/vol/30},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 30, 2009},