High-fidelity surrogate wind data

The objective of this project is to create a modelling framework for high-fidelity surrogate wind data. This work is largely the PhD project of Daniele D’Ambrosio whom I supervise with prof.  Tim De Troyer.

The increasing sophistication of wind turbine design and control generates a need for high-quality data. Therefore, the relatively limited set of measured wind data needs to be extended with computer-generated surrogate data, e.g. to make reliable statistical studies of energy production and mechanical loads. Surrogate data should realistically reflect the full scale of possible wind conditions. First, the surrogate data should conform to the same statistical descriptors as measured physical data (in particular the amplitude distribution and the frequency spectrum).

There are methods that can handle this. A much bigger challenge is non-stationarity, reflected by the variation of wind conditions on distinct temporal scales (such as daily or seasonal variations). No existing method can fully capture this non-stationarity. Furthermore, wind data are three-dimensional, in the sense that information about the wind direction is as important as information about the wind speed. In this application, we outline a unified mathematical framework to tackle the twin challenges of non-stationarity and three- dimensionality. The framework allows to generate non-stationary, three-dimensional wind time series with high fidelity.

Such a framework is generic and can be used to generate surrogate data for other non-stationary processes, such as ocean waves or pollutant dispersion.

A preprint of a paper outline the approach is available through arXiv.