Cluster analysis of wind turbine alarms for characterising and classifying stoppages
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Accepted Version
Date
2018-07
Authors
Leahy, Kevin
Gallagher, Colm V.
O'Donovan, Peter
O'Sullivan, Dominic T. J.
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Published Version
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.
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Keywords
Feature extraction , Pattern classification , Pattern clustering , Wind power plants , Wind turbines , Wind turbine alarm system , Cluster analysis , Stoppage classification , Stoppage characterization , Alarm sequences , Sequence associated characteristics , Silhouette analysis , Manual inspection , Information overload
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
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Copyright
© 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.