  This study presents a Bayesian parametric model for the purpose of estimating
the extreme load on a wind turbine. The extreme load is the highest stress
level imposed on a turbine structure that the turbine would experience during
its service lifetime. A wind turbine should be designed to resist such a high
load to avoid catastrophic structural failures. To assess the extreme load,
turbine structural responses are evaluated by conducting field measurement
campaigns or performing aeroelastic simulation studies. In general, data
obtained in either case are not sufficient to represent various loading
responses under all possible weather conditions. An appropriate extrapolation
is necessary to characterize the structural loads in a turbine's service life.
This study devises a Bayesian spline method for this extrapolation purpose,
using load data collected in a period much shorter than a turbine's service
life. The spline method is applied to three sets of turbine's load response
data to estimate the corresponding extreme loads at the roots of the turbine
blades. Compared to the current industry practice, the spline method appears to
provide better extreme load assessment.
