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Thursday, 29 September 2016
11:30 - 13:00 Component reliability and diagnostics: early detection and intervention is key!
O&M & logistics  
Onshore      Offshore    

Room: Hall G1

This session will give an insight into wind turbine reliability and the state of the art in wind turbine diagnostics. It will look at how to collect and analyse reliability data in order to improve design as well as O&M strategies. This session will also look at the use of supervisory control and data acquisition (SCADA) data for condition monitoring: both 'conventional' ten minute data and higher frequency 1Hz data. Finally, there will be a case study of how power data from the converter can be used to infer wind turbine loading.

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Simon Watson, Professor of Wind Energy, Loughborough University, United Kingdom
Matthew Hostetler, Industrial Internet Solutions Manager, Sentient Science, United States

Abstract ID: 134 science & research
Christopher Smith
Postgraduate Researcher, Durham University, United Kingdom
Monitoring wind turbine loading using power converter signals

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Abstract ID: 161
Berthold Hahn
Head of Department, Fraunhofer IWES, Germany
Recommended practices for data collection, reliability assessment and O&M optimisation

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Abstract ID: 229 science & research
Stefan Faulstich
Group Manager, Fraunhofer IWES, Germany
Modelling the failure behaviour of wind turbines

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Abstract ID: 336
Elizabeth Traiger
Senior Researcher, DNV GL, United Kingdom
Give it all you got: using big data machine learning ensembles for condition monitoring

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Abstract ID: 361
Henrik Pedersen
Manager, Siemens Wind Power A/S, Denmark
Reduce production loss through early-stage detection of abnormal behaviour

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