Site or regional design wind speeds?

Estimates of high retur n period design wind speeds are required to achieve an acceptable probabilit y of failure. These can be found by fitting an appropriate distribution to observed data but is complicated by spatial variation of the wind climate, therefore it is unclear which data are relevant at a particular location. We aim to frame this problem in terms of model selection and the bias variance trade-off. Existing models for incorporating site versus regionally averaged statistics are expressed in terms of key parameters, which allow the errors associated with each model to be derived. It is found that using a characteristic wind speed in codification combines the site statistics (low bias) with regionally averaged statistics (low variance). The ret u r n period of the characteristic wind speed is shown to act as a parameter controlling the relative weighting of these models. This insight is used to develop an optimal (minimu m mean square error) estimator of the design wind speed, which is shown to vary based on both the available quantity of data at a particular site and how wel l this data corresponds to the regional average. Some practical advantages of this optimal model are then demonstrated at several South African stations.

Automated summary

This study aims to solve the challenge of spatial variation in wind climate for wind power design by framing it as a model selection and bias variance trade-off problem. By incorporating both site statistics and regionally averaged statistics through the use of characteristic wind speed, an optimal estimator of design wind speed is developed. This estimator varies based on the available data quantity at a specific site and its correspondence to the regional average.