Please click on...
to download the presentation
to listen to the speaker audio recording (available after the event)
to download the abstract or full paper
Speaker name to view their biography
| THURSDAY, 29 SEPTEMBER 2016 09:00 - 10:30 LOADS AND FATIGUE Room: Hall G2 | Turbine technology |
|
Session description In this session, participants will hear about the latest developments in wind turbine loading and system behaviour. Presentations will cover a variety of topics - from modelling approaches to turbine control. The research work presented brings new approaches which will help the industry increase the reliability of turbines and reduce installation costs, especially offshore. Presented results will rely on real-world measurements and operational data. You attended this session?
|
Co-chair:  Michael Muskulus, Norwegian University of Science and Technology (NTNU), Norway Fabian Vorpahl, Senvion, Germany |
|
Ricardo Faerron Guzmán Stuttgart Wind Energy, Germany RECOMMENDATIONS FOR LOAD VALIDATION OF AN OFFSHORE WIND TURBINE WITH THE USE OF STATISTICAL DATA: EXPERIENCE FROM ALPHA VENTUS Abstract ID: 183 |
Download presentation:
123_WindEurope2016presentation.pdf (1.10 MB)
Download full paper: PDF (0.88 MB)
|
|
Narasimhan Sampath Kumar Atkins Limited, United Kingdom APPROACH TO WIND WAVE CORRELATION IN COUPLED ANALYSIS OF OFFSHORE WTG SUBSTRUCTURES Abstract ID: 293 |
Download presentation:
119_WindEurope2016presentation.pptx (3.66 MB)
Download full paper: PDF (0.76 MB)
|
|
Rasoul Shirzadeh ForWind, Germany APPLICATION OF TWO PASSIVE STRATEGIES ON THE LOAD MITIGATION OF LARGE OFFSHORE WIND TURBINES Abstract ID: 300 |
Download presentation:
121_WindEurope2016presentation.ppt (5.49 MB)
Download full paper: PDF (1.29 MB)
|
|
Carlos Gonzalez SgurrControl, United Kingdom FIELD TESTS OF INDIVIDUAL BLADE CONTROL AND ITS IMPACT ON THE WIND TURBINE COMPONENTS LIFETIME Abstract ID: 404 |
Download presentation:
122_WindEurope2016presentation.pptx (4.59 MB)
Download full paper: PDF (0.35 MB)
|
|
Sebastian Kaus Senvion, Germany MACHINE LEARNING ALGORITHMS FOR WIND TURBINE POWER PERFORMANCE MONITORING – TRACKING OPTIMAL YAW ALIGNMENT BASED ON SCADA DATA Abstract ID: 463 |
Download presentation:
120_WindEurope2016presentation.pptx (0.56 MB)
Full paper not available |
Back to top
Follow EWEA on: