Diagnosing and predicting wind turbine faults from SCADA data using support vector machines

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dc.contributor.authorLeahy, Kevin
dc.contributor.authorHu, R. Lily
dc.contributor.authorKonstantakopoulos, Ioannis C.
dc.contributor.authorSpanos, Costas J.
dc.contributor.authorAgogino, Alice M.
dc.contributor.authorO'Sullivan, Dominic T. J.
dc.contributor.funderScience Foundation Ireland
dc.contributor.funderUS-UK Fulbright Commission
dc.contributor.funderSustainable Energy Authority of Ireland
dc.contributor.funderNational Research Foundation Singapore
dc.contributor.funderAlexander S. Onassis Public Benefit Foundation
dc.date.accessioned2018-03-09T12:55:07Z
dc.date.available2018-03-09T12:55:07Z
dc.date.issued2018
dc.description.abstractUnscheduled or reactive maintenance on wind turbines due to component failure incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to perform maintenance before it's needed. To date, a strong effort has been applied to developing Condition Monitoring Systems (CMSs) which rely on retrofitting expensive vibration or oil analysis sensors to the turbine. Instead, by performing complex analysis of existing data from the turbine's Supervisory Control and Data Acquisition (SCADA) system, valuable insights into turbine performance can be obtained at a much lower cost. In this paper, fault and alarm data from a turbine on the Southern coast of Ireland is analysed to identify periods of nominal and faulty operation. Classification techniques are then applied to detect and diagnose faults by taking into account other SCADA data such as temperature, pitch and rotor data. This is then extended to allow prediction and diagnosis in advance of specific faults. Results are provided which show recall scores generally above 80\% for fault detection and diagnosis, and prediction up to 24 hours in advance of specific faults, representing significant improvement over previous techniques.en
dc.description.sponsorshipNational Research Foundation Singapore (Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleidUNSP 006
dc.identifier.citationLeahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J., Agogino, A. M. and O’Sullivan, D. T. (2018) 'Diagnosing and predicting wind turbine faults from SCADA data using support vector machines', International Journal of Prognostics and Health Management, 9(1), 006 (11pp).en
dc.identifier.endpage11
dc.identifier.issn2153-2648
dc.identifier.issued1
dc.identifier.journaltitleInternational Journal of Prognostics and Health Managementen
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/5607
dc.identifier.volume9
dc.language.isoenen
dc.publisherPrognostics and Health Management Societyen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2302/IE/Marine Renewable Energy Ireland (MaREI) - The SFI Centre for Marine Renewable Energy Research/
dc.relation.urihttps://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2017/ijphm_18_006.pdf
dc.rights© 2018, Kevin Leahy et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/us/
dc.subjectWind turbineen
dc.subjectSCADA dataen
dc.subjectFaultsen
dc.subjectDiagnosisen
dc.subjectVector machinesen
dc.titleDiagnosing and predicting wind turbine faults from SCADA data using support vector machinesen
dc.typeArticle (peer-reviewed)en
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