Abstract. The present study extends the idea of the vertical-axis wind turbine (VAWT) “vortex generator mode” for wake recovery on a wind farm scale, working towards the concept of “regenerative wind farming”, where upstream turbines entrain vertical momentum for those downstream. An experimental wind tunnel demonstration of the regenerative wind farming concept for an array of nine H-type VAWTs arranged in a 3×3 grid layout is performed. Volumetric particle tracking velocimetry measures the wake within the simulated wind farm while using two vortex generator modes achieved through a fixed blade pitch. The results demonstrate the strong dependence of the wake topology of a VAWT on the streamwise vorticity system, which can be effectively modified by pitching the blades and subsequently changing the load distribution of the different quadrants of a VAWT. An increase in momentum entrainment in the wake is observed for both vortex generator modes of operation, highlighting the potential of achieving regenerative wind farming. The derived available power within the farm increases by factors of 6.4 and 2.1 for the pitch-in and pitch-out cases compared to the baseline case, respectively, considering potential rotors directly in line with those upwind.

Abstract. Models used in wind resource assessment (WRA) range from engineering wake models and computational fluid dynamics models to mesoscale weather models with wind farm parameterizations and, more recently, large-eddy simulation (LES). The latter two produce time series of wind farm power of a certain period. This simulation period is, in the case of LES, mostly limited to ≤ 1 year due to the computational costs. However, estimates of long-term (O(10 years)) power production are of high value to many parties involved in WRA. To address the need to calculate long-term annual energy production from ≤ 1-year model runs, therefore, this paper presents methods to estimate the long-term (O(10 years)) power production of a wind farm using a ≤ 1-year simulation. To validate the methods, a 10-year LES of a hypothetical large offshore wind farm is performed. The methods work by estimating the conditional probability densities between wind farm power from the LES and wind speed from reanalysis data (ERA5) from a short (≤ 1 year) LES run. The conditional probability densities are then integrated over 10 years of ERA5 wind speed, yielding an estimate of the long-term mean power production. This “long-term correction” method is validated on varying simulation periods, selected with four different day-selection techniques. When applied to a simulation period of 365 consecutive days, the methods can estimate the 10-year mean power production with a mean absolute error of around 0.35 % of the long-term mean. When choosing the simulation period with day-selection techniques that represent the long-term climate, only roughly 200 simulation days are needed to achieve the same accuracy. Finally, a method to also include wind observations in the long-term correction is presented and tested. This requires an additional “free stream” LES run without active turbines and gives estimates of long-term power and wind that are corrected for a potential LES bias. Although validation of this final approach is difficult in the employed modeling strategy, it gives valuable insights and fits within the common WRA practice of combining models and observations. The presented techniques are based on physical arguments, computationally cheap, and simple to implement. Furthermore, they are not limited to LES but can be applied to other time-series-based models. As such, they could be a useful extension for the diverse set of modeling, observational, and statistical techniques used in WRA.

Abstract. Large-scale wind turbines offer higher power output but present design challenges as increased blade flexibility affects aerodynamic performance and loading under varying conditions. Although flexible structures are considered in terms of (periodic) load control and aerodynamic stability, the impact of flexibility on the aerodynamic response of the blades is currently not fully addressed in conventional control strategies. The current state-of-the-art control strategy is the tip-speed ratio tracking scheme, which aims to maximise power production in the partial-load region by maintaining a constant ratio between blade velocity and wind speed. However, this approach fails under large deformations, where the deflection and structural twist of the blade impact aerodynamic performance. This work aims to redefine the state-of-the-art wind turbine control with the COntrol scheme for FLEXible wind turbines (COFLEX): a novel feedforward–feedback control scheme that leverages optimal operational set points computed by COFLEXOpt, which is a set point optimiser considering the effects of blade deformations on aerodynamic performance and turbine loading. The proposed combined strategy consists of two key modules. The first module, COFLEXOpt, is an optimisation framework that provides controller set points while allowing constraints to be imposed on various operational, structural, and load properties, such as blade deflection and other structural loads. Set points obtained using COFLEXOpt are agnostic to operating regions, meaning that the operating region boundaries are optimised rather than prescribed. The second module is a feedforward–feedback controller and uses the set point mappings generated with COFLEXOpt, scheduled on wind speed estimates, to evaluate feedforward inputs and feedback to correct modelling inaccuracies and ensure closed-loop stability. A set point smoothing technique enables smooth transitions from partial- to full-load operations. The IEA 15 MW turbine is used as an exemplary case to show the effectiveness of COFLEX in maximising rotor aerodynamic efficiency while imposing blade out-of-plane tip displacement constraints. An analysis of the steady-state optimisation results shows that accounting for blade flexibility leads to variable optimal tip-speed ratio operating points in the partial-load region, and the collective pitch angle can be used to counteract blade torsion, maximising power coefficient while complying with imposed constraints. The established controller, tailored to track these optimised set points and operating points, was evaluated through time-marching mid-fidelity HAWC2 simulations across the entire operational range of the IEA 15 MW reference wind turbine (RWT). These simulations, performed under uniform and turbulent wind inflows, demonstrate excellent agreement between optimised steady states and median values obtained from HAWC2 simulations. Furthermore, the generator power shows an increase of up to 5 % in the partial-load region compared to the reference scheme while maintaining blade deflection at a similar level.

Abstract. The dynamic response of wind turbine rotor blades can result in multi-axial, non-proportional stress histories in the adhesive joints. These are not properly considered in current design guidelines and standards. In this paper, the concept of non-proportionality factors is utilized for the quantification of the degree of non-proportionality. A novel formulation is proposed and verified, followed by its application to the analysis of trailing edge adhesive joints of three different blade designs with varying blade sizes. Two simple fatigue analysis concepts, one based on global equivalent stresses and the other on the search for a critical plane, are evaluated with respect to their ability to account for multi-axial, non-proportional stress time series. A clear correlation of the degree of non-proportionality with the blade size was not found, as the blade response depends on not only the turbine size but also the blade design philosophy. A remarkable degree of non-proportionality was observed for all three blades. The fatigue damage criteria employed in this study revealed that the critical-plane approach can deal with non-proportional, multi-axial stress time series and therefore is more precise when non-proportionality is involved. The global equivalent stress approach seems to provide conservative predictions. However, there are implications from the literature that global equivalent stress approaches can result in non-conservative estimates under specific circumstances, so further research including validation of appropriate models is necessary for final conclusions.

Abstract. Parked loads are a major design load case for vertical axis wind turbines (VAWTs) because of persistent high loads on the rotor when in standstill conditions. This paper examines the aerodynamic parked loads of model-scale floating troposkein VAWTs tested in a wind–wave basin to support development and validation of a parked load model for floating VAWTs. We analyze the effects of wind speed, turbine solidity (varying number of blades), and rotor azimuth on parked loads and investigate the impact of different wind–wave platform conditions (comparing locked (fixed tower base) versus floating cases with and without waves). The experimental results indicate that parked loads (for both locked and floating platform conditions) and the amplitude of turbine tilting increase with the wind speed, which is expected. The parked loads also increase with the increase in solidity; however, the variation in loads in a revolution decreases for three blades versus two blades. If only aerodynamic parked loads are considered, the turbine with a floating platform exhibits lower parked loads compared to turbine with a locked platform (fixed base) due to the effect of the tilted condition of the floating platform. Moreover, comparison between floating with wind–wave and floating with wind only cases shows that although both exhibit parked loads of similar magnitude, the former exhibits more high-frequency variation due to coupled effects of wind, wave, and floating platform dynamics. Additionally, we present a semi-numerical tool for estimating parked loads of VAWTs that we improved and validated to predict the floating parked loads. The semi-numerical model accurately predicts the parked load behavior of VAWTs for the range of effects noted above. The datasets from this experimental work can serve as benchmarks for validating other computational parked load estimating tools, while the semi-numerical tool provides a way to gain insight into the unmeasurable quantities.

Abstract. Mesoscale model predictions of wind, turbulence, and wind energy capacity factors are evaluated in the Altamont Pass Wind Resource Area of California (APWRA), where the diurnal regional sea breeze and associated terrain-driven speedup flows drive wind energy production during the summer months. Results from the Weather Research and Forecasting model version 4.4 using a novel three-dimensional planetary boundary layer (3D PBL) scheme, which treats both vertical and horizontal turbulent mixing, are compared to those using a well-established one-dimensional (1D) scheme that treats only vertical turbulent mixing. Each configuration is evaluated over a nearly 3-month-long period during the Hill Flow Study, and due to the recurring nature of the observed speedup flows, diurnal composite averaging is used to capture robust trends in model performance. Both model configurations showed similar overall skill. The general timing and direction of the speedup flows is captured, but their magnitude is overestimated within a typical wind turbine rotor layer. Both also fail to capture a persistent observed near-surface jet-like flow, likely due to the limited grid resolution that is typical of mesoscale models. However, the 3D PBL configuration shows several minor improvements over the 1D PBL configuration, including improved wind speed and turbulence kinetic energy profiles during the accelerating phase of the speedup events, as well as reduced positive wind speed bias at surface stations across the APWRA region. Using a mesoscale wind farm parameterization, modeled capacity factors are also compared to monthly data reported to the US Energy Information Administration (EIA) during the study period. Although the monthly trend in the data is captured, both model configurations overestimate capacity factors by roughly 7 %–11 %. Through model evaluation, this study provides confidence in the 3D PBL scheme for wind energy applications in complex terrain and provides guidance for future testing.

Abstract. Offshore wind speed data around wind turbine hub heights are fairly limited, available through in situ observations from wind masts, sonic detection and ranging (sodar) instruments, or floating light detection and ranging (lidar) buoys at selected locations or as forecasting-model-based output from reanalysis products. In situ wind profiles have sparse geospatial coverage and are costly to obtain en masse, whereas satellite-derived 10 m wind speeds have vast coverage at high resolution. In this study, we show the benefit of deploying machine learning techniques, in particular random forest regression (RFR), over conventional methods for accurately estimating offshore wind speed profiles on a high-resolution (0.25°) grid at 6-hourly resolution from 1987 to the present using satellite-derived surface wind speeds from the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Blended Seawinds version 2.0 (NBSv2.0) product. We use wind profiles from five publicly available lidar datasets over the northeastern US and Californian coasts to train and validate an RFR model to extrapolate wind speed profiles up to 200 m. A single extrapolation model applicable to the coastal regions of the contiguous US and Hawai`i is developed instead of site-specific models attempted in previous studies. The model outperforms conventional extrapolation methods at the training locations as well as at two additional lidar and six National Offshore Wind (NOW)-23 stations that are independent of the training locations, especially under conditions of high vertical wind shear and at wind turbine hub heights (∼ 100 m). The final model is applied to the NBSv2.0 data from 1987 to the present to create 6-hourly wind speed profiles over the coastal regions of the contiguous US and Hawai`i on a 0.25° grid, which are shown to outperform NOW-23 and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) at 100 m using a correlated triple-collocation method over 5 years of matchup data (2015–2019). Gridded maps of wind profiles in the marine boundary layer over US coastal waters will enable the development of a suite of wind energy resources and will help stakeholders in their decision-making related to wind-based renewable energy development.

Abstract. Wind farm flow control (WFFC) is the discipline of manipulating the flow between wind turbines to achieve a farm-wide goal, like power maximization, power tracking or load mitigation. Specifically, steady-state control approaches have shown promising results in both theory and practice for power maximization. But how are they expected to perform in a dynamically changing environment? This paper presents an open-source wake modeling framework called OFF (abbreviated from the models OnWARDS, FLORIDyn and FLORIS). It allows the approximation of the performance of WFFC strategies in response to environmental changes at a low computational cost. It is rooted in previously published dynamic parametric engineering models and offers a flexible and adaptable platform to explore these models further. The presented study tests the modeling framework by investigating the performance of different wake steering controllers in a 10-turbine wind farm case study based on a subset of the Dutch wind farm Hollandse Kust Noord (HKN). The case study uses a 24 h wind direction time series based on field data and verifies subsets of the time series in a large-eddy simulation (LES). The results highlight how dependent yaw travel is on the controller settings and suggest where users can strike a balance between power gains and actuator usage. They also show the structural differences and similarities between steady-state and dynamic engineering models. The comparison to LES shows what timescales the surrogate models cover and how accurately. While steady-state models capture turbine power signal dynamics up to ≈1/570 Hz, the dynamic wake description can predict dynamics up to ≈1/360 Hz with a better correlation and normalized root-mean-square error. Further results show that the dynamic wake description is mainly advantageous over steady-state wake models for shorter periods (< 20 min). The paper also opens up discussion about the effectiveness of wind farm flow control in a time-marching manner as opposed to a steady-state viewpoint.

Abstract. Wake steering strategies are employed to increase the overall power production of wind farms by deflecting wakes of upstream turbines away from downstream ones. The gain in net power comes at the expense of increased fatigue loads experienced by downstream turbines. In this work we investigate performance and fatigue loading characteristics of a small farm consisting of five aligned International Energy Agency Wind Technology Collaboration Programme 15 MW wind turbines. A parametric study is performed where, for every wind direction from −20 to 20°, the yaw misalignment angle varies from −25 to 25°. This setup allows us to investigate asymmetries and identify optimal conditions for a given wind direction. In general, we find that positive yaw configurations are preferred and that yaw configurations that result in attractive power differences when compared to a baseline no-yaw scenario (25 %) come with significant increase in fatigue loading (we use the standard deviation and damage-equivalent load (DEL) of the blade-root, low-speed shaft, and tower-base moments as proxies for fatigue load). We find that for any given positive wind inflow angle, yaw angles between −2.5 and 15° yield power differences of 10 %–20 % over a no-yaw baseline, and positive yaw is preferred because of lower fatigue loading. For any given negative wind inflow angles, positive yaw also results in lower magnitudes of standard deviation and DEL for the channels investigated. A small power loss of up to 2 % is observed for some positive yaw angles under negative wind directions (as compared to symmetric negative yaw and positive wind cases), but improvements in terms of loads exceed 25 % and may be enough to justify a positive yaw configuration under negative winds as well. We show that such behavior can be explained by partial waking and the direction of the rotation of the rotor.

Abstract. The size growth of modern wind turbines creates challenges in their control system design, particularly due to greater wind variability across larger rotor areas. As modern turbine control systems rely on the availability of accurate wind speed information, the increasing unrepresentativeness of pointwise measurement devices, such as anemometers, necessitates the incorporation of more representative rotor-effective wind speed (REWS) estimation. Classical REWS estimators, based on static power relations, often fail to account for dynamic changes, leading to inaccurate estimation. To overcome these challenges, this paper introduces a power-balance-based REWS estimation framework and splits the estimation problem into two modules: an aerodynamic power estimator and a wind speed estimate solver. Two possible aerodynamic power estimation techniques are discussed based on numerical derivative and state estimation. As state estimator calibration remained a challenge for varying wind turbine sizes, a gain-tailoring method for the performance calibration throughout a range of modern wind turbine sizes has been derived for the state-estimation-based aerodynamic power estimator. Two types of wind speed estimate solvers are analyzed, namely the continuous and iterative single-step methods. From the two modules, the best-performing methods – the state estimation aerodynamic power estimator and iterative single-step wind speed solver – are chosen to form the optimal power balance REWS estimator. The combined optimal estimator is validated through OpenFAST simulations of the National Renewable Energy Laboratory (NREL) 5 MW and IEA 22 MW turbines and compared against a baseline method. The proposed method demonstrates good tracking of the REWS, better noise resilience, and convenient estimator gain calibration across different turbine sizes.