Authors: Houda Chouiref; Boumedyen Boussaid; Mohamed Naceur Abdelkrim; Vicenç Puig; Christophe Aubrun
Addresses: National School of Engineers, University of Gabès, Omar Ibn Khattab Road, 6029 Gabès, Tunisia ' National School of Engineers, University of Gabès, Omar Ibn Khattab Road, 6029 Gabès, Tunisia ' National School of Engineers, University of Gabès, Omar Ibn Khattab Road, 6029 Gabès, Tunisia ' Advanced Control Systems Group, Technical University of Catalonia, Pau Gargallo, 5, 08028, Barcelona, Spain ' Research Center of Automatic Nancy, University of Lorraine, BP 239, 54506 Vandoeuvre Les Nancy, France
Abstract: In order to keep away wind turbines from catastrophic conditions due to sudden breakdowns, it is important to detect faults as soon as possible. For diagnosis, a model-based approach is chosen. There are many works that use this fault detection design, but the majority of them consider this system as a linear time invariant (LTI) model. The objective of this paper is, first, to find an LPV model of the system using the subspace identification technique of linear parameter-varying (LPV). Second, we focus on fault diagnosis based on residual generation which is obtained as a comparison between the measured variable and the estimated one using this LPV model. In this work, a benchmark of a wind turbine case is proposed with six predefined faults (three sensor fault scenarios, two actuator fault scenarios and a system fault scenario).
Keywords: model-based fault diagnosis; wind turbine; LPV modelling; subspace identification; residue.
International Journal of Modelling, Identification and Control, 2017 Vol.27 No.4, pp.243 - 255
Available online: 09 Jun 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article