With the growing offshore wind industry, ramp events, i.e., significant variation in wind speed (or power) within a short time period, have become of critical importance to the end-users. The analysis of wind power ramp events from the perspective of grid operators or energy traders is prevalent in the literature. Nonetheless, this area of research is still evolving. The criterion of a minimum of turbines and its effect on the detection of ramp events, thresholds, or smoothing of power differences have not been considered so far. Moreover, little research has been conducted on analyzing the impact of ramp events on ultimate wind turbine loads. To the best of the authors’ knowledge, the results have not yet been validated with measurements. Therefore, the primary objective of this report is to investigate the impact of wind-induced ramp events on offshore wind farm power and ultimate wind turbine loads by using offshore measurements. At first, a wind power ramp detection algorithm is developed by considering the suitable power normalization technique and the criterion of a minimum number of turbines. The detected ramp events followed a decreasing trend in a number of events with the increasing wind speed, turbulence intensity, ramp duration, and wind direction changes. This can be because of the strong dependence of the number of ramp events on the non-linearity of the power curve. However, the available measurement data sets were found to be insufficient to capture the correlation and travel time of ramp events within two wind farms. Therefore, detailed meteorological information with the high spatial and temporal resolution is necessary for thorough analysis. Following that, extreme wind speed fluctuations (ramp-like events) were found to be extreme load drivers for wind speed bins that fall under the inactive pitch or above rated wind speed region. The measured loads are further compared with the simulation results, which are obtained by following the International Electrotechnical Commission (IEC) guidelines of extreme turbulence load cases. In general, the measured loads associated with the extreme fluctuations did not outperform the simulations, except for the tower side-side moments. Besides, extreme loads around rated wind speed exceed the simulations, but the values are not related to the extreme fluctuations. Therefore, this study examined the high-frequency time series for the most interesting blade root flap-wise moments. Based on the investigation, extreme loads over the wind speed range, except near rated wind speed, were governed by extreme turbulence or ramp events involving a sudden pitch transition from the inactive to the active region. The extreme loads around rated wind speed, which exceeded the simulations, were associated with the standard deviations equivalent to the normal turbulence but with comparatively higher fluctuation frequency. In conclusion, for the ultimate load analysis, the wind speed time series should include a sudden transition of a pitch angle from the inactive to the active region.

Wind turbines installed in a wind farm are typically subject to increased turbulence because they are in the wake of upstream wind turbines that generate additional turbulence. Accurate prediction of turbulence in wind farms is critical as it is proportional to wind turbine fatigue loads, power losses and prediction of wind farm lifetime. IEC Standard 61400-1 suggests the use of the semi-empirical turbulence model called Frandsen model, which was originally proposed in 1999. Since the development of the Frandsen model, the size of wind turbines and wind farms has increased significantly. Therefore, this work aims to determine the accuracy of two versions of the Frandsen model: Standard and Modified, when applied to large offshore wind farms experiencing a combination of atmospheric stability conditions. This is done by comparing the estimated wind farm turbulence under specific atmospheric stability conditions with measurements from Westermost Rough and Horns Rev 2. It was found that the atmospheric stability distribution and the distance of the upstream wake inducing wind turbine at the offshore site plays a significant role in the accuracy of the estimated turbulence from the Frandsen models. The estimated turbulence intensity from the Standard and Modified Frandsen models was found to be under-predicted with respect to the measured turbulence for all atmospheric stability conditions at both wind farms. For wind directions with wake flow for upstream wind turbines more than 10 rotor diameters away, the Modified Frandsen model showed better prediction of turbulence intensity compared to the Standard Frandsen model. On the other hand, for wind directions with wake flow for upstream wind turbines less than 10 rotor diameters away, the Standard Frandsen model showed better turbulence intensity estimation compared to the Modified Frandsen model. Among all atmospheric stability conditions, the turbulence intensity estimate was closest to the measured data for unstable conditions. It is suggested that this fact can be attributed to the presence of a significant number of unstable conditions in the offshore wind farms used for the design of the semi-empirical Frandsen model.