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dc.contributor.authorVidal Seguí, Yolanda
dc.contributor.authorPozo Montero, Francesc
dc.contributor.authorLuo Ren, Ningsu
dc.contributor.authorTutivén Gálvez, Christian
dc.contributor.authorRodellar Benedé, José
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2018-09-28T09:26:38Z
dc.date.issued2018
dc.identifier.citationVidal, Y., Pozo, F., Luo, N., Tutivén, C., Rodellar, J. Machine learning techniques for wind turbine fault diagnosis. A: World Conference on Structural Control and Monitoring. "Control and monitoring: abstracts and papers of the 7th World Conference on Structural Control and Monitoring: 7WCSCM: July 22-25, 2018 Qingdao, China". 2018, p. 1385-1394.
dc.identifier.urihttp://hdl.handle.net/2117/121613
dc.description.abstractThe reliability requirements of wind turbine (WT) components have increased significantly in recent years in the search for a lower impact on the cost of energy. In addition, the trend towards larger WTs installed in offshore locations has significantly increased the cost of repair of the components. In the wind industry, therefore, condition monitoring is crucial for maximum availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available sensors of the SCADA system, a data-driven multi-fault diagnosis strategy is contributed. The advanced WT benchmark proposed by [1] is used. That is a 5 MW modern WT simulated with the FAST [2] software and subject to various actuators and sensors faults of different type. The measurement noise at each sensor is modeled as a Gaussian white noise. First, the SCADA measurements are pre-processed and feature transformation based on multiway principal component analysis (MPCA) is realized. Then, 10-fold cross validation support vector machines (SVM) based classification is applied. In this work, SVMs were used as a first choice for fault detection as they have proven their robustness for some particular faults [3-5] but never accomplished, to the authors’ knowledge, at the same time the detection and classification of all the proposed faults taken into account in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results show that all studied faults are detected and classified with an overall accuracy of 98%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for real-time condition monitoring in WTs.
dc.format.extent10 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Energies
dc.subjectÀrees temàtiques de la UPC::Física
dc.subject.lcshMachine learning
dc.subject.lcshSupport vector machines
dc.subject.lcshWind turbines
dc.subject.othermachine learning support vector machines fault diagnosis health monitoring wind turbine
dc.titleMachine learning techniques for wind turbine fault diagnosis
dc.typeConference lecture
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacAerogeneradors
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.rights.accessRestricted access - publisher's policy
drac.iddocument23331681
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorVidal, Y., Pozo, F., Luo, N., Tutivén, C., Rodellar, J.
upcommons.citation.contributorWorld Conference on Structural Control and Monitoring
upcommons.citation.publishedtrue
upcommons.citation.publicationNameControl and monitoring: abstracts and papers of the 7th World Conference on Structural Control and Monitoring: 7WCSCM: July 22-25, 2018 Qingdao, China
upcommons.citation.startingPage1385
upcommons.citation.endingPage1394


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