Title: Towards an efficient early warning system for extreme wind speed detection
Authors: Daniela Castro-Camilo - University of Glasgow (United Kingdom) [presenting]
Raphael Huser - King Abdullah University of Science and Technology (Saudi Arabia)
Amanda Hering - Baylor University (United States)
Abstract: Renewable sources of energy such as wind power have become a sustainable alternative to fossil fuels-based energy. However, the uncertainty and fluctuation of wind speeds brings a great threat to the wind power production stability, and to the wind turbines themselves. A turbine cut-off point denotes how fast the turbine can go before turbine blades are brought to rest to prevent any damage produced by extreme wind speeds. Therefore, one of the main problems related to extremes in wind is to forecast when the wind speed will exceed this cut-off speed. We develop a flexible early warning system to detect the risk of extreme winds at a given station based on a set of neighboring stations and the dominant wind regime. The main challenges with this approach are the temporal non-stationarity of the wind regimes, and the fact that wind speeds are inherently anisotropic, which implies that the set of neighboring stations can potentially be different with different wind regimes. To cope with these issues we fit a flexible spatial copula model to threshold exceedances, in order to determine the probability of observing an extreme event at a specific site, given the dynamic wind regimes and the information gathered from a set of neighboring stations. Our approach is illustrated with data measured at meteorological towers located in the Pacific Northwest, US.