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Using a Langevin model for the simulation ofenvironmental conditions in an offshore wind farm
Helene Seyr and Michael Muskulus
January 18, 2018
Helene Seyr Langevin process for weather modeling
Outline
Introduction
Methodology
Data
Results
Conclusions
Helene Seyr Langevin process for weather modeling
Introduction
O&M (cost) optimization is focus of researchMany simulation models/optimizations rely on artificiallygenerated weather time series to test different strategiesNovel approach to model significant wave height and windspeedLangevin process:
Equations fitted to the dataUsed to generate artificial weather
Helene Seyr Langevin process for weather modeling
Langevin process
Deterministic contribution
F = D(1)
Stochastic contribution
G =√D(2)Γt
Helene Seyr Langevin process for weather modeling
Data
ECMWF:Re-analysis6h resolutionDogger Bank wind farm37 years
Fino 1:Measurement from met-mast and buoy10min/30min meansAlpha Ventus wind farm6 years
Helene Seyr Langevin process for weather modeling
Results I
Helene Seyr Langevin process for weather modeling
Results II
Helene Seyr Langevin process for weather modeling
Results III
Helene Seyr Langevin process for weather modeling
Results IV
Helene Seyr Langevin process for weather modeling
Results V
Helene Seyr Langevin process for weather modeling
Results VI
Helene Seyr Langevin process for weather modeling
Conclusions
Langevin process is a good alternativeProperties of waves represented very well (Distribution,Persistence)Higher sampling frequency → better model2D Langevin process for correlation (?)
Helene Seyr Langevin process for weather modeling
Thank you for your attention
Helene Seyr
PhD Candidate
+47 400 86 761
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