Cluster analysis of wind turbine alarms for characterising and classifying stoppages

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dc.contributor.authorLeahy, Kevin
dc.contributor.authorGallagher, Colm V.
dc.contributor.authorO'Donovan, Peter
dc.contributor.authorO'Sullivan, Dominic T. J.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2018-09-18T11:13:52Z
dc.date.available2018-09-18T11:13:52Z
dc.date.issued2018-07
dc.date.updated2018-09-18T11:02:29Z
dc.description.abstractTurbine alarm systems can give useful information to remote technicians on the cause of a fault or stoppage. However, alarms are generally generated at much too high a rate to gain meaningful insight from on their own, so generally require extensive domain knowledge to interpret. By grouping together commonly occurring alarm sequences, the burden of analysis can be reduced. Instead of analysing many individual alarms that occur during a stoppage, the stoppage can be linked to a commonly occurring sequence of alarms. Hence, maintenance technicians can be given information about the shared characteristics or root causes of stoppages where that particular alarm sequence appeared in the past. This research presents a methodology to identify relevant alarms from specific turbine assemblies and group together similar alarm sequences as they appear during stoppages. Batches of sequences associated with 456 different stoppages are created, and features are extracted from these batches representing the order the alarms appeared in. The batches are grouped together using clustering techniques, and evaluated using silhouette analysis and manual inspection. Results show that almost half of all stoppages can be attributed to one of 15 different clusters of alarm sequences.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationLeahy, K., Gallagher, C., O'Donovan, P. and O'Sullivan, D. T. J. (2018) 'Cluster analysis of wind turbine alarms for characterising and classifying stoppages', IET Renewable Power Generation, 12(10), pp. 1146-1154. doi:10.1049/iet-rpg.2017.0422en
dc.identifier.doi10.1049/iet-rpg.2017.0422
dc.identifier.endpage1154en
dc.identifier.issn1752-1416
dc.identifier.issued10en
dc.identifier.journaltitleIET Renewable Power Generationen
dc.identifier.startpage1146en
dc.identifier.urihttps://hdl.handle.net/10468/6801
dc.identifier.volume12en
dc.language.isoenen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Supplement/12/RC/2302s/IE/Marine Renewable Energy Ireland (MaREI) - EU Grant Manager/en
dc.rights© 2017, The Institution of Engineering and Technology. This paper is a postprint of a paper submitted to and accepted for publication in IET Renewable Power Generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.en
dc.subjectFeature extractionen
dc.subjectPattern classificationen
dc.subjectPattern clusteringen
dc.subjectWind power plantsen
dc.subjectWind turbinesen
dc.subjectWind turbine alarm systemen
dc.subjectCluster analysisen
dc.subjectStoppage classificationen
dc.subjectStoppage characterizationen
dc.subjectAlarm sequencesen
dc.subjectSequence associated characteristicsen
dc.subjectSilhouette analysisen
dc.subjectManual inspectionen
dc.subjectInformation overloaden
dc.titleCluster analysis of wind turbine alarms for characterising and classifying stoppagesen
dc.typeArticle (peer-reviewed)en
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