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dc.contributor.authorFernández Canti, Rosa M.
dc.contributor.authorBlesa Izquierdo, Joaquim
dc.contributor.authorTornil Sin, Sebastián
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2016-03-07T15:29:46Z
dc.date.available2016-03-07T15:29:46Z
dc.date.issued2015-10-30
dc.identifier.citationFernández-Cantí, R. M., Blesa, J., Tornil-Sin, S., Puig, V. Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach. "Annual reviews in control", 30 Octubre 2015, vol. 40, p. 56-59.
dc.identifier.issn1367-5788
dc.identifier.urihttp://hdl.handle.net/2117/83898
dc.description.abstractThis paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.
dc.format.extent4 p.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria mecànica::Motors::Turbines
dc.subjectÀrees temàtiques de la UPC::Energies::Energia eòlica
dc.subject.lcshTurbines -- Automatic control
dc.subject.lcshWind power
dc.subject.otherControl theory
dc.subject.otherFault detection and isolation
dc.subject.otherBayesian reasoning
dc.subject.otherSet-membership approaches
dc.subject.otherWind turbine benchmark
dc.subject.otherUncertainty
dc.titleFault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach
dc.typeArticle
dc.subject.lemacTurbines -- Control automàtic
dc.subject.lemacEnergia eòlica
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy
dc.contributor.groupUniversitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1016/j.arcontrol.2015.08.002
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S1367578815000395
dc.rights.accessOpen Access
local.identifier.drac17411338
dc.description.versionPreprint
local.citation.authorFernández-Cantí, R. M.; Blesa, J.; Tornil-Sin, S.; Puig, V.
local.citation.publicationNameAnnual reviews in control
local.citation.volume40
local.citation.startingPage56
local.citation.endingPage59


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