As more floating farms are being developed, the wake interaction between multiple floating wind turbines (FWTs) is becoming increasingly relevant. FWTs have long natural periods in certain degrees of freedom, and the large-scale movement of the wake, known as wake meandering, occurs at very low frequencies. In this study, we use FAST.Farm to simulate a two-turbine case with three different FWT concepts: a semisubmersible (semi), a spar, and a tension leg platform (TLP), separated by eight rotor diameters in the wind direction. Since wake meandering varies depending on the environmental conditions, three different wind speeds (for all three concepts) as well as two different turbulence levels (for the semi) are considered. For the below-rated wind speed, when wake meandering was most extreme, yaw motion standard deviations for the downstream semi were approximately 40% greater in high turbulence and over 100% greater in low turbulence when compared with the upstream semi. The low yaw natural frequency (0.01 Hz) of the semi was excited by meandering, while quasi-static responses resulted in approximately 20% increases in yaw motion standard deviations for the spar and TLP. Differences in fatigue loading between the upstream and downstream turbines for the mooring line tension and tower base fore-aft bending moment mostly depended on the velocity deficit and were not directly affected by meandering. However, wake meandering did affect fatigue loading related to the tower top yaw moment and the blade root out-of-plane moment. KEYWORDS coherence, dynamic wake meandering, fatigue, floating wind turbine, wind farm modeling 1 INTRODUCTION Floating wind turbines (FWTs) are an emerging technology to harness the wind resource in deep water where bottom-fixed turbines are no longer economically viable. There have been several demonstration projects of stand-alone FWTs, but there is only one floating wind farm, HywindScotland. 1 Several other floating wind farms are currently in the planning stage, and as more floating wind farms are developed, modeling the wake interaction between FWTs is becoming increasingly relevant.Wind turbine operation induces a downstream decrease in wind speed and an increase in turbulence intensity in the form of a wake. There are many observations of the effect of the wake on power production and structural loading. 2-7 Moreover, the wake tends to meander in certain atmospheric conditions. This large-scale movement of the wake was observed as early as the 1980s by Ainslie. 8 Ainslie noticed that variability in wind direction gave rise to meandering, particularly in nonstable atmospheric conditions. Recent high-and mid-fidelity simulations 9,10 have also shown that wake meandering depends on atmospheric stability. In addition to atmospheric stability, wake meandering is likely to vary due to wind speed, turbulence, shear, veer, etc.Wakes are commonly modeled with computationally inexpensive steady, or quasi-steady, wake modeling tools to optimize wind farm layout with respect to annual energy produc...
Abstract. Terrain-induced flow phenomena modulate wind turbine performance and wake behavior in ways that are not adequately accounted for in typical wind turbine wake and wind plant design models. In this work, we simulate flow over two parallel ridges with a wind turbine on one of the ridges, focusing on conditions observed during the Perdigão field campaign in 2017. Two case studies are selected to be representative of typical flow conditions at the site, including the effects of atmospheric stability: a stable case where a mountain wave occurs (as in ∼ 50 % of the nights observed) and a convective case where a recirculation zone forms in the lee of the ridge with the turbine (as occurred over 50 % of the time with upstream winds normal to the ridgeline). We use the Weather Research and Forecasting Model (WRF), dynamically downscaled from the mesoscale (6.75 km resolution) to microscale large-eddy simulation (LES) at 10 m resolution, where a generalized actuator disk (GAD) wind turbine parameterization is used to simulate turbine wakes. We compare the WRF–LES–GAD model results to data from meteorological towers, lidars, and a tethered lifting system, showing good qualitative and quantitative agreement for both case studies. Significantly, the wind turbine wake shows different amounts of vertical deflection from the terrain and persistence downstream in the two stability regimes. In the stable case, the wake follows the terrain along with the mountain wave and deflects downwards by nearly 100 m below hub height at four rotor diameters downstream. In the convective case, the wake deflects above the recirculation zone over 40 m above hub height at the same downstream distance. Overall, the WRF–LES–GAD model is able to capture the observed behavior of the wind turbine wakes, demonstrating the model's ability to represent wakes over complex terrain for two distinct and representative atmospheric stability classes, and, potentially, to improve wind turbine siting and operation in hilly landscapes.
While most existing modeling and analysis of floating wind turbines (FWTs) considers isolated systems, interactions among multiple FWTs arranged in an array have received little attention. In this study, two 10 MW semi-submersible FWTs, separated by 8 rotor diameters (D) in the wind direction, are simulated with an ambient wind speed of 10 m/s and in moderate wave conditions using FAST. Farm to investigate the effects of wakes on global responses. Synthetic inflow is generated using three methods: the Kaimal turbulence model, 1) without and 2) with spatial coherence in the lateral and vertical velocity components, and 3) the Mann turbulence model (where spatial coherence in all three dimensions is inherent to the model). The first method results in negligible wake meandering, a relatively uniform wake deficit, while the second and third methods result in meandering of the upstream turbine’s lateral wake center at the downstream turbine’s rotor plane of up to approximately 1D and 1.5D, respectively. The slow meandering behavior of the upstream turbine’s wake resulted in increased low-frequency platform motions for the downstream turbine. Yaw motions were especially susceptible to wake meandering as the standard deviation of the downstream turbine’s yaw motion increased by 28.0 % for the second method and 11.3 % for the third method. Increased low-frequency response in structural loading was also observed. Wake effects led to between 2 % and 30 % greater fatigue damage at the top of the tower for all three methods and at the base of the tower for the second method. However, other results were found to be sensitive to the blade-passing frequency.
Global dynamic response models used for the design of wind turbines are largely based on neutral stability, which is not representative of real atmospheric conditions. Offshore wind farms, for example, have been seen to experience predominantly unstable conditions, especially at lower wind speeds. In the current work, we use four wind generation models under stable, neutral and unstable atmospheric stability conditions to study the low‐frequency content of the global responses of a semisubmersible floating wind turbine (FWT). To represent the wind fields, we use the Kaimal Spectrum and Exponential Coherence Model (Kaimal), the Mann Spectral Tensor Model (Mann), a point measurement based model (TIMESR) and large‐eddy simulation (LES). At the low‐frequency range, both atmospheric stability and the turbulence wind model significantly affect the response of the FWT. In all the cases studied throughout this paper, the structural response under unstable conditions is higher than under stable or neutral conditions. The TIMESR and the Kaimal models fitted to the FINO‐1 offshore meteorological mast measurements show more similar responses than the Mann model; surge and pitch are higher for the TIMESR and Kaimal models, and yaw is lower. When fitted to LES, TIMESR and Kaimal predict surge and pitch responses closer to LES, but they underestimate the responses related to yaw, opposite to what the Mann model does. The responses are directly related to turbulence intensity and coherence, which are affected by atmospheric stability. Therefore, based on the analyses carried out through this study, the structural analysis of FWT should account for the effect of atmospheric stability.
Abstract. Most detailed modeling and simulation studies of wind turbine wakes have considered flat terrain scenarios. Wind turbines, however, are commonly sited in mountainous or hilly terrain to take advantage of accelerating flow over ridgelines. In addition to topographic acceleration, other turbulent flow phenomena commonly occur in complex terrain, and often depend upon the thermal stratification of the atmospheric boundary layer. Enhanced understanding of wind turbine wake interaction with these terrain-induced flow phenomena can significantly improve wind farm siting, optimization, and control. In this study, we simulate conditions observed during the Perdigão field campaign in 2017, consisting of flow over two parallel ridges with a wind turbine located on top of one of the ridges. We use the Weather Research and Forecasting model (WRF) nested down to micro-scale large-eddy simulation (LES) at 10 m resolution, with a generalized actuator disk (GAD) wind turbine parameterization to simulate turbine wakes. Two case studies are selected, a stable case where a mountain wave occurs and a convective case where a recirculation zone forms in the lee of the ridge with the turbine. The WRF-LES-GAD model is validated against data from meteorological towers, soundings, and a tethered lifting system, showing good agreement for both cases. Comparisons with scanning Doppler lidar data for the stable case show that the overall characteristics of the mountain wave are well-captured, although the wind speed is underestimated. For the convective case, the size of the recirculation zone within the valley shows good agreement. The wind turbine wake behavior shows dependence on atmospheric stability, with different amounts of vertical deflection from the terrain and persistence downstream for the stable and convective conditions. For the stable case, the wake follows the terrain along with the mountain wave and deflects downwards by nearly 100 m below hub-height at four rotor diameters downstream. For the convective case, the wake deflects above the recirculation zone over 50 m above hub-height at the same downstream distance. This study demonstrates the ability of the WRF-LES-GAD model to capture the expected behavior of wind turbine wakes in regions of complex terrain, and thereby to potentially improve wind turbine siting and operation in hilly landscapes.
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