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THURSDAY, 29 SEPTEMBER 2016 11:30 - 13:00 COMPONENT RELIABILITY AND DIAGNOSTICS: EARLY DETECTION AND INTERVENTION IS KEY! Room: Hall G1 |
O&M & logistics |
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Session description 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. You attended this session?
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Co-chair:  Simon Watson, Loughborough University, United Kingdom Matthew Hostetler, Sentient Science, United States |
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Christopher Smith Durham University, United Kingdom MONITORING WIND TURBINE LOADING USING POWER CONVERTER SIGNALS Abstract ID: 134 Download presentation: 138_WindEurope2016presentation.pptx (2.24 MB)
Full paper not available |
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Berthold Hahn Fraunhofer IWES, Germany RECOMMENDED PRACTICES FOR DATA COLLECTION, RELIABILITY ASSESSMENT AND O&M OPTIMISATION Abstract ID: 161 Download presentation: 135_WindEurope2016presentation.pptx (2.90 MB)
Full paper not available |
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Stefan Faulstich Fraunhofer IWES, Germany MODELLING THE FAILURE BEHAVIOUR OF WIND TURBINES Abstract ID: 229 Download presentation: 134_WindEurope2016presentation.pptx (4.38 MB)
Download full paper: PDF (0.31 MB)
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Elizabeth Traiger DNV GL, United Kingdom GIVE IT ALL YOU GOT: USING BIG DATA MACHINE LEARNING ENSEMBLES FOR CONDITION MONITORING Abstract ID: 336 Download presentation: 137_WindEurope2016presentation.pdf (0.27 MB)
Full paper not available |
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Henrik Pedersen Siemens Wind Power A/S, Denmark REDUCE PRODUCTION LOSS THROUGH EARLY-STAGE DETECTION OF ABNORMAL BEHAVIOUR Abstract ID: 361 Download presentation: 136_WindEurope2016presentation.pptx (11.12 MB)
Download full paper: PDF (0.94 MB)
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