Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31108
Fault Classification of a Doubly FED Induction Machine Using Neural Network

Authors: A. Ourici


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.

Keywords: Neural Network, doubly fed induction machine, inter turn stator fault, open phase fault, Park's vector approach, PWMinverter

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1383


[1] A. Masmoudi, A. Toumi & M. Kamoun, "Power analysis and efficiency optimization of a doubly fed synchronous machine", Proc. Electric Machines and Power Systems 21, pp 473-491, 1993.
[2] Y. Kawabata, E. Ejiogu & T. Kawabata, "Vector controlled double inverter fed wound rotor induction motor suitable for high power drives .IEEE Trans. Industry Applications 35 no. 5, pp 1058-1066, sept./oct 1999.
[3] Paul Etienne VIDAL, "Commande non linéaire d-une machine asynchrone ├á double alimentation", Thesis of doctorate, Toulouse France, december 2004.
[4] S.M.A. Cruz and A.J. Marque Cardoso, " Stator winding fault diagnosis in three phase synchronous and asynchronous motors, by the Extended Park-s Vector Approach", IEEE Trans on Industry applications, vol. 37, No5, sept/oct 2001.
[5] Mohamed El Hachemi Benbouzid, "A review of induction motors signature analysis as a medium for faults detection", IEEE Trans on Industrial Electronics, vol 47 No 5, October 2000.
[6] S. Chen and S.A. Billings, "Neural network for nonlinear system modeling and identification", Int. J. Control, vol. 56, No 2, pp 319-346, Aug 1992.