Cluster analysis of wind turbine alarms for characterising and classifying stoppages

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dc.contributor.author Leahy, Kevin
dc.contributor.author Gallagher, Colm V.
dc.contributor.author O'Donovan, Peter
dc.contributor.author O'Sullivan, Dominic T. J.
dc.date.accessioned 2018-09-18T11:13:52Z
dc.date.available 2018-09-18T11:13:52Z
dc.date.issued 2018-07
dc.identifier.citation Leahy, 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.2017.0422 en
dc.identifier.volume 12 en
dc.identifier.issued 10 en
dc.identifier.startpage 1146 en
dc.identifier.endpage 1154 en
dc.identifier.issn 1752-1416
dc.identifier.uri http://hdl.handle.net/10468/6801
dc.identifier.doi 10.1049/iet-rpg.2017.0422
dc.description.abstract Turbine 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.format.mimetype application/pdf en
dc.language.iso en 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.subject Feature extraction en
dc.subject Pattern classification en
dc.subject Pattern clustering en
dc.subject Wind power plants en
dc.subject Wind turbines en
dc.subject Wind turbine alarm system en
dc.subject Cluster analysis en
dc.subject Stoppage classification en
dc.subject Stoppage characterization en
dc.subject Alarm sequences en
dc.subject Sequence associated characteristics en
dc.subject Silhouette analysis en
dc.subject Manual inspection en
dc.subject Information overload en
dc.title Cluster analysis of wind turbine alarms for characterising and classifying stoppages en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Dominic O'Sullivan, Civil Engineering, University College Cork, Cork, Ireland. +353-21-490-3000 Email: dominic.osullivan@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2018-09-18T11:02:29Z
dc.description.version Accepted Version en
dc.internal.rssid 453061688
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle IET Renewable Power Generation en
dc.internal.copyrightchecked Yes en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress dominic.osullivan@ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres Supplement/12/RC/2302s/IE/Marine Renewable Energy Ireland (MaREI) - EU Grant Manager/ en


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