Abstract
For the planning of operations and maintenance in offshore wind farms, many simulation models exist. Many rely on artificially generated weather time series to test different strategies. In this paper, we present a novel approach to modeling both the significant wave height and wind speed based on measurements from the site. We use a stochastic process called the Langevin process. First, equations are fitted to the available data, which are then used to generate the artificial weather data. The properties of these artificial weather time series are very close to the properties of the actual weather. Mean and standard deviation as well as the overall distribution and seasonality can be captured by the new model. Additionally, the persistence of waves and winds is replicated. This is especially important, as the length of weather windows is an important factor in operation and maintenance planning.
Highlights
Both in research and the wind industry, simulation models are often used to improve the operations and maintenance for offshore wind farms
We present a novel approach to modeling both the significant wave height and wind speed based on measurements from the site
Not every investigation is shown for both sites, the performance of the model is similar in both cases and we have chosen to show plots from different sites in order to represent different weather conditions
Summary
Both in research and the wind industry, simulation models are often used to improve the operations and maintenance for offshore wind farms. In order to model a specific location, without the risk of finding an optimal solution for a specific historical weather dataset, some researchers want to use artificial weather data. This artificial weather data should represent the given location and have the same properties, such as annual mean wind speeds or persistence of wave heights. Even if a model can theoretically be used with historic weather data, this data is not available of an appropriate length and quality for some locations In this case, researchers benefit from artificial weather time series.
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