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Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach
dc.contributor.author | Fernández Canti, Rosa M. |
dc.contributor.author | Blesa Izquierdo, Joaquim |
dc.contributor.author | Tornil Sin, Sebastián |
dc.contributor.author | Puig Cayuela, Vicenç |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.date.accessioned | 2016-03-07T15:29:46Z |
dc.date.available | 2016-03-07T15:29:46Z |
dc.date.issued | 2015-10-30 |
dc.identifier.citation | Fernández-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.issn | 1367-5788 |
dc.identifier.uri | http://hdl.handle.net/2117/83898 |
dc.description.abstract | This 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.extent | 4 p. |
dc.language.iso | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria mecànica::Motors::Turbines |
dc.subject | Àrees temàtiques de la UPC::Energies::Energia eòlica |
dc.subject.lcsh | Turbines -- Automatic control |
dc.subject.lcsh | Wind power |
dc.subject.other | Control theory |
dc.subject.other | Fault detection and isolation |
dc.subject.other | Bayesian reasoning |
dc.subject.other | Set-membership approaches |
dc.subject.other | Wind turbine benchmark |
dc.subject.other | Uncertainty |
dc.title | Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach |
dc.type | Article |
dc.subject.lemac | Turbines -- Control automàtic |
dc.subject.lemac | Energia eòlica |
dc.contributor.group | Universitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy |
dc.contributor.group | Universitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control |
dc.contributor.group | Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
dc.identifier.doi | 10.1016/j.arcontrol.2015.08.002 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S1367578815000395 |
dc.rights.access | Open Access |
local.identifier.drac | 17411338 |
dc.description.version | Preprint |
local.citation.author | Fernández-Cantí, R. M.; Blesa, J.; Tornil-Sin, S.; Puig, V. |
local.citation.publicationName | Annual reviews in control |
local.citation.volume | 40 |
local.citation.startingPage | 56 |
local.citation.endingPage | 59 |
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