For measurements taken over a decade at the coastal Danish site Høvsøre, we find the variance associated with wind speed events from the offshore direction to exceed the prescribed extreme turbulence model (ETM) of the International Electrotechnical Commission (IEC) 61400-1 Edition 3 standard for wind turbine safety. The variance of wind velocity fluctuations manifested during these events is not due to extreme turbulence; rather, it is primarily caused by ramp-like increases in wind speed associated with larger-scale meteorological processes. The measurements are both linearly detrended and high-pass filtered in order to investigate how these events – and such commonly used filtering – affect the estimated 50-year return period of turbulence levels. High-pass filtering the measurements with a cutoff frequency of 1∕300 Hz reduces the 50-year turbulence levels below that of IEC ETM class C, whereas linear detrending does not. This is seen as the high-pass filtering more effectively removes variance associated with the ramp-like events. The impact of the observed events on a wind turbine are investigated using aeroelastic simulations that are driven by constrained turbulence simulation fields. Relevant wind turbine component loads from the simulations are compared with the extreme turbulence load case prescribed by the IEC standard. The loads from the event simulations are on average lower for all considered load components, with one exception: ramp-like events at wind speeds between 8 and 16 m s⁻¹, at which the wind speed rises to exceed rated wind speed, can lead to high thrust on the rotor, resulting in extreme tower-base fore–aft loads that exceed the extreme turbulence load case of the IEC standard.

Summary: The Global Atlas for Siting Parameters project compiles a suite of models into a complex modeling system, uses up‐to‐date global datasets, and creates global atlases of siting parameters at a spatial resolution of 275 m. These parameters include the 50‐year wind, turbulence, and turbine class recommendations based on relevant generic turbines. The suite of models includes the microscale Linear Computational Model (LINCOM), a statistical, spectral correction method here revised for strong convective areas and tropical cyclone affected areas, two turbulence models with four setups, and load models. To this complexity, an uncertainty model was developed to classify the various sources of uncertainties for both the extreme wind and the turbulence calculations, and accordingly, atlases of uncertainty classification were created. Preliminary validation of the global calculations of the 50‐year wind and turbulence is done through comparisons with measurements, and the results are promising. This is the first time the siting parameters are obtained with such a high spatial resolution and shared on open data portal. It is expected to benefit the global wind energy planning and development.

The present study introduces a new method to characterize ramp-like wind speed fluctuations, including coherent gusts. This method combines two well-known methods: the continuous wavelet transform and the fitting of an analytical form based on the error function. The method provides estimation of ramp amplitude and rise time, and is herein used to statistically characterize ramp-like fluctuations at three different measurement sites. Together with the corresponding amplitude of wind direction change, the ramp amplitude and rise time variables are compared to the extreme coherent gust with direction change from the IEC wind turbine safety standard. From the comparison we find that the observed amplitudes of the estimated fluctuations do not exceed the one prescribed in the standard, but the rise time is generally much longer, on average around 200 s. The direction change does however exceed the one prescribed in the standard several times, but for those events the rise time is a minute or more. We also demonstrate a general pattern in the statistical behaviour of the characteristic ramp variables, noting their wind speed dependence, or lack thereof, at the different sites.

Leading-edge erosion (LEE) can significantly impact the aerodynamic performance of wind turbines and thereby the overall efficiency of a wind farm. Typically, erosion is modeled for individual turbines where aerodynamic effects only impact the energy production through degraded power curves. For wind farms, aerodynamic deficiency has the potential to also alter wake dynamics, which will affect the overall energy production. The objective of this study is to demonstrate this combined effect by coupling LEE damage prediction and aerodynamic loss modeling with steady-state wind farm flow modeling. The modeling workflow is used to simulate the effect of LEE on the Horns Rev 1 wind farm. Based on a 10-year simulation, the aerodynamic effect of LEE was found to be insignificant for the first few years of operation but rapidly increases and reaches a maximum annual energy production (AEP) loss of 2.9 % in the last year for a single turbine. When including the impact of LEE to the wakes behind eroded turbines, the AEP loss is seen to reduce to 2.7 % at the wind farm level, i.e., corresponding to an overestimation of the AEP loss of up to 7 % when only considering a single wind turbine. In addition, it was demonstrated that the modeling framework can be used to prioritize turbines for an optimal repairing strategy.