Publications

@conference{Chouiref2016,

title = {LPV model-based fault detection: Application to wind turbine benchmark},

author = {H. Chouiref and B. Boussaid and M. N. Abdelkrim and V. Puig and C. Aubrun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965079867\&doi=10.1109%2fICMIC.2015.7409464\&partnerID=40\&md5=a6d5dfccedf490ed1fef53cc80ec3ff4},

doi = {10.1109/ICMIC.2015.7409464},

isbn = {9780956715753},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings of 2015 7th International Conference on Modelling, Identification and Control, ICMIC 2015},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

school = {ENIG},

abstract = {In this paper, we present a fault detection approach of a linear parameter varying (LPV) system using residuals generator based on the use of the predictor based LPV subspace identification approach (LPV PBSID). To address this problem of fault detection in the pitch subsystem considered in the wind turbine benchmark introduced in IFAC SAFEPROCESS 2009, the failure events are caused by jumps in the damping ratio and natural frequency values of the model. The damping ratio is increased and the natural frequency is decreased with the hydraulic pressure variation. Satisfactory results have been obtained of the proposed approach using the proposed fault scenarios. © 2015 University of Al Qayrawan, Tunisia.},

note = {cited By 2},

keywords = {Benchmarking; Damping; Hydraulic motors; Natural frequencies; Wind turbines, Damping ratio; Detection approach; Failure events; Fault scenarios; Hydraulic pressure; Linear parameter varying systems; LPV models; Subspace identification, Fault detection},

pubstate = {published},

tppubtype = {conference}

}