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

helene.seyr@ntnu.no

+47 400 86 761

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