Multivariate analysis of extreme metocean conditions for offshore wind turbines

Abstract

Most offshore wind turbines (OWTs) are designed according to the international standard IEC 61400-3 which requires consideration of several design load cases under 50-year extreme storm conditions during which the wind turbine is not operational (i.e. the rotor is parked and blades are feathered). Each of these load cases depends on combinations of at least three jointly distributed metocean parameters, the mean wind speed, the significant wave height, and the peak spectral period. In practice, these variables are commonly estimated for the 50-year extreme storm using a simple but coarse method, wherein 50-year values of wind speed and wave height are calculated independently and combined with a range of peak spectral period conditioned on the 50-year wave height. The IEC Standard does not provide detailed guidance on how to calculate the appropriate range of peak spectral period. Given the varying correlation of these parameters from site-to-site, this approach is clearly an approximation which is assumed to overestimate structural loads since wind and wave are combined without regard to their correlation. In this paper, we introduce an alternative multivariate method for assessing extreme storm conditions. The method is based on the Nataf model and the Inverse First Order Reliability Method (IFORM) and uses measurements or hindcasts of wind speed, wave height and peak spectral period to estimate an environmental surface which defines combinations of these parameters with a particular recurrence period. The method is illustrated using three sites along the U.S. Atlantic coast near Maine, Delaware and Georgia. Mudline moments are calculated using this new multivariate method for a hypothetical 5MW OWT supported by a monopile and compared with mudline moments calculated using simpler univariate approaches. The results of the comparison highlight the importance of selecting an appropriate range of the peak spectral period when using the simpler univariate approaches.