NDLI: Additive and multiplicative fault diagnosis for a doubly-fed induction generator

Content Provider	IEEE Xplore Digital Library
Author	Boulkroune, B. Galvez-Carrillo, M. Kinnaert, M.
Copyright Year	2011
Description	Author affiliation: Department of Control Engineering and System Analysis, Université Libre de Bruxelles (ULB), CP 165/55, 50 Av. F.D. Roosevelt, 1050 Brussels, Belgium (Boulkroune, B.; Galvez-Carrillo, M.; Kinnaert, M.)
Abstract	The problem of additive and multiplicative fault detection and isolation (FDI) in the current sensors of a doubly-fed induction generator (DFIG) is considered in the presence of model uncertainty. A residual generator based on the DFIG model is proposed using the structure of the classical generalized observer scheme (GOS). However, each observer in this scheme is replaced by a robust H−/H∞ fault detection filter followed by a Kalman-like observer. The latter further attenuates the effect of the modelling uncertainties on the residuals. It exploits the specific pattern induced by the balanced three phase nature of all the electric signals. It turns out that the fault detection/isolation problem then amounts to detecting an abrupt change in the mean of the residual vector in the additive fault case, or the appearance of sine waves superimposed to a white noise vector in the multiplicative fault case. A decision algorithm made of a combination of generalized likelihood ratio (GLR) algorithms allows us to detect and isolate the additive and multiplicative sensor faults. The complete FDI system is tested through simulations on a controlled DFIG.
Starting Page	1302
Ending Page	1308
File Size	991270
Page Count	7
File Format	PDF
ISBN	9781457710629
ISSN	10851992
e-ISBN	9781457710636
e-ISBN	9781457710612
DOI	10.1109/CCA.2011.6044473
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Additives Sensors Observers Rotors Fault detection Uncertainty Robustness
Content Type	Text
Resource Type	Article

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