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An LPV modelling and fault diagnosis in wind turbine benchmark system
by Houda Chouiref; Boumedyen Boussaid; Mohamed Naceur Abdelkrim; Vicenç Puig; Christophe Aubrun
International Journal of Modelling, Identification and Control (IJMIC), Vol. 27, No. 4, 2017
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).
Online publication date: Fri, 09-Jun-2017
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