RAVE Workshop 2021

The RAVE Workshop 2021 took place January 28, 2021 in an online format. Presentations were given on the most recent and up to date research at alpha ventus. 

Please refer and download the program to see the workshop's main topics in 2021. You can find the presentations below.

If you are interested in updates on the next workshop 2022, please send us an e-mail to the following address:

RAVE Workshop 2021 - Presentations


Opening remarks

Bernhard Lange
Fraunhofer-Institut für Windenergiesysteme

Presentation 2: "A view back on 10 years of research at alpha ventus from the perspective of the funding agency"

Daniela Bizjak
Project Management Jülich

Presentation 3: "The FINO1 Platform - Offshore Research Laboratory in the North Sea"

Lisa Schulz, Björn Lehmann-Matthaei
FuE-Zentrum FH Kiel GmbH

Presentation 4: "Data Quality Management in the RAVE project, introducing machine learning to the process"

BSH (with DNV GL and UL international)

Presentation 5: "Wind-Wave correlation in the German Bight as a logistical planning tool for offshore activities"

Lukas Fröhling
Leibniz University Hannover

Presentation 6: "Wind farm wake effects on the wind conditions and the fatigue loads of the offshore wind farm alpha ventus"

Marcos Ortensi
UL international

Presentation 7: "Minute-scale forecasting of wind power using a long-range LiDAR in alpha ventus“"

Ines Würth
University of Stuttgart

Presentation 8: "Measuring large scale offshore wind farm effects with scanning LiDAR"

Jörge Schneemann
University of Oldenburg

Presentation 9: "Structural load validation for wake situations using alpha ventus measurement data"

Matthias Kretschmer
University of Stuttgart

Presentation 10: "Influence of the environmental conditions on the acceleration response of the tower/hub structure of the AV07 wind turbine"

Etienne Cheynet
University of Bergen

Presentation 11: "Wind turbine monitoring & lifetime extension (IEA Wind Task 42)"

Tanja Grießmann
Leibniz University Hannover

Presentation 12: "Interpretable Machine Learning for load prediction“"

Artur Movsessian
University of Edinburgh