Turbine Load Research Articles

Sensitivity of the Prediction of Wind Turbine Wakes to the Sub-Grid Scale Model

In a large-eddy simulation (LES) approach, the sub-grid scale (SGS) model accounts for the contribution of eddies and their fluxes whose length scales are smaller than the filter width. In wind turbine and farm simulations, different SGS models have been adopted, but their impact on turbine performance and wake prediction remains unknown for non-neutrally stable atmospheric boundary layers. Here, large-eddy simulations of an NREL–5MW wind turbine in stable atmospheric conditions are performed with six SGS models: standard Smagorinsky, Lagrangian-Averaged Scale-Dependent Dynamic (LASDD), Wall-Adapting Local Eddy-Viscosity, Turbulent Kinetic Energy, Stability Dependent Smagorinsky, and Anisotropic Minimum-Dissipation (AMD) models. The resolved flow field and turbine loading have shown limited sensitivity to the SGS model with some deviations from the LASDD in wind speed and turbulence intensity at the turbine elevation. This limited sensitivity is owed to the adopted high-resolution grid necessary to provide an acceptable resolution for the actuator-line method. Regarding the computational costs, the LASDD model has the highest compute overhead to the LES compared to the other five SGS models. The AMD model is simple to implement and provides three-dimensional variation of the SGS eddy-viscosity without any parameter tuning, thus it has the highest potential to be used in LES of wind turbines and farms operating in stable conditions.

Enhanced shear and veer in the Taiwan Strait during typhoon passage

During typhoon passage extreme wind conditions pose a challenge to the structural integrity of wind turbines. Particularly, wind shear and wind veer can influence wind turbine loads. This study investigates how Taiwan’s central mountain range affects wind shear and wind veer in the Taiwan Strait during three westward-moving typhoon cases. The typhoons are simulated with the Weather Research and Forecasting (WRF) model. We find that wind speed, shear, and veer vary over different regions in the Taiwan Strait. In large areas, the simulated wind shear is larger than modeled over the open ocean. In particular, mountain blockage leads to a spatially confined area of several 1000 km2 downstream of Taiwan, that exhibits over several hours strongly modified shear and veer in all three analyzed cases. Shear exponents up to 0.75 and veer between 0.2 and 0.6° m−1 suggest that turbine loads are impacted by the vertical change in the wind field in this area. The shear exponent and wind veer vary strongly with height in the downstream area of Taiwan’s central mountain range. The location of the area with large wind shear and wind veer differs between the three simulated typhoon cases and primarily depends on the latitude of the typhoon track relative to the central mountain range.

Impact of low-frequency fluctuations on loads of a fixed-bottom offshore reference wind turbine

Marine atmospheric turbulence is predominantly characterized by large-scale structures associated with low-frequency wind turbulence. However, conventional turbulence models for assessing wind turbine loads often fall short of adequately integrating these low-frequency wind fluctuations. In this study, we used a model for low-frequency wind fluctuations to generate wind fields representative of offshore wind conditions in the North Sea. The IEA 15 MW reference wind turbine was subjected to three distinct incoming wind fields: high-frequency (3D) turbulence, low-frequency (2D) turbulence combined with high-frequency (3D) turbulence, and 3D turbulence scaled by target or measured standard deviation of wind components. We found that increased damage equivalent loads by incorporating the low-frequency wind fields are only significant for the tower base fore-aft and blade root fore-aft moments. The impact of low-frequency wind turbulence on the damage equivalent loads was more pronounced for below-rated wind speeds. The scaled 3D turbulence yielded the highest lifetime damage equivalent loads, i.e., in the range of 8-18% more than the unscaled 3D turbulence and combined 2D +3D turbulence. This method leads to an overestimation of loads and, hence, more expensive turbines. Incorporating 2D+3D turbulence in wind fields resulted in a 3.8% increase in lifetime damage equivalent loads for the tower base fore-aft and blade root flapwise moments compared to unscaled 3D turbulence. These findings underscore the importance of considering low-frequency wind fluctuations in the turbine design process, especially for a more accurate estimation of loads.

Wind Farm Wakes and Farm-to-Farm Interactions: Lidar and Wind Tunnel Tests

Recent experimental and numerical evidence has shown that the cumulative wake generated from the overlapping of multiple wakes within a wind farm could reduce power performance and enhance fatigue loads of wind turbines installed in neighboring downstream wind farms and may also extend up to distances one order of magnitude larger than those typically considered for intra-farm wake interactions. Similar to individual wind turbine wakes, wind farm wakes have a velocity deficit and added turbulence intensity, both affected by the turbine rotor thrust forces and the incoming turbulence intensity. Therefore, the evolution of wind farm wakes will vary for different operational and atmospheric conditions. In this paper, lidar measurements collected during the American WAKE experimeNt (AWAKEN) and wind tunnel tests of wind farms reproduced by porous disks are leveraged to investigate wind farm wakes.

Impact of wake steering on loads of downstream wind turbines at an above-rated condition

Wake steering strategies often seek to gain power at the expense of increased fatigue loads. Here, we investigate the feasibility of applying wake steering at an above-rated condition. In such a condition, the farm is operating at rated power, and thus, increased power output is not the goal. Instead, wake steering is considered in the context of load reduction. We perform a sweep of wind directions and yaw misalignment angles, ranging from negative to positive values. This approach allows us to obtain trends and identify asymmetries in turbine response for symmetric scenarios. We use a wind farm consisting of five aligned IEA Wind 15-MW reference wind turbines, and analyze trends related to the blade-root, low-speed shaft, and tower-base moments, both in terms of standard deviation and damage equivalent loads. We show that for any given fixed wind direction, the turbines can be yawed such that the fatigue loads are reduced. Reductions of up to 5% (depending on the component) in terms of standard deviation and damage equivalent loads can be achieved by negatively yawing the turbine. A negative yaw misalignment has shown to be the direction of larger improvements. Such results contrast those found for below-rated conditions, where a positive yaw misalignment is typically preferred. However, since load reduction is not uniform across all component loads, more study and consideration is required before operational recommendations can be made.

Validation of new and existing methods for time-domain simulations of turbulence and loads

We seek to obtain a second-by-second match between the simulated and measured structural loads of a utility-scale wind turbine. To obtain the one-to-one load simulations, we start with the furthest upstream component of the modeling chain: the turbulent inflow. We consider new and existing methods to generate constrained-turbulence flow fields. The new method is based on large-eddy simulations (LES) and machine learning (ML). The existing methods include Kaimal-based TurbSim and the superstatistical wind field model. The inflow measurements used to constrain these simulations are obtained with a nacelle-mounted scanning lidar. We compare the flow fields for the different inflow simulation approaches and validate their associated load predictions against measurements collected in the Rotor Aero-dynamics, Aeroelastics, and Wake (RAAW) field campaign. We find that the rotor-position control developed for this study is key in enabling the time match between measurements and simulations. When this control approach is used, the load simulation performance tracks with the inflow simulation fidelity, with LES+ML yielding errors ≤ 4% for the damage-equivalent loads of flapwise bending moment, and tower fore-aft bending moments.

Wind tunnel inflow data assimilation in an LES digital twin for improving validation against an experimental wind turbine wake

Wind energy is playing an essential role in the energy transition towards a carbon-neutral system, for which wind turbine wakes are one of the most important aspects. By extracting energy from the flow, the turbine generates power but also decreases the production and increases the loading of downstream wind turbines; and potentially affects nearby structures.To better understand the physical phenomenon, Large Eddy Simulation (LES) offers scale-resolving capabilities and a physical insight into complex turbulence flows. The state of the art in terms of high-fidelity wake simulations consists in combining LES with an Actuator Line Model in order to capture the tip vortices and detailed wake dynamics.This paper focuses on the comparison between such simulation, with experiments in the controlled environment of a wind tunnel, in order to assess the performance and limitations of the model. A large part is dedicated to reproducing a developing flow, with a controlled level of turbulence. The Recycle and Rescale Method is used to provide the mean inflow profile, while a novel approach using controlled volume forces allows to improve the turbulence level of the flow. With the proposed methodology, the mean profile is properly imposed, while the associated turbulence characteristics is improved by roughly 50 %.

A control-oriented load surrogate model based on sector-averaged inflow quantities: capturing damage for unwaked, waked, wake-steering and curtailed wind turbines

This paper presents a novel approach to constructing a load surrogate model. The surrogate estimates the damage equivalent loads (DELs) of a wind turbine of a given type regardless of its position within a wind farm, and under various farm control actions. The model relies solely on local inflow quantities (sector-averaged wind speeds and turbulence intensities) and local control parameters (rotor speed, pitch angle, and yaw misalignment). Despite its highly simplified representation of the complex behavior of the turbulent wind field, wake effects, and controller dynamics, these quantities prove sufficient to characterize DELs. The paper demonstrates the training of this load model within a simulation environment. Validation results using a different wind farm configuration indicate that the surrogate can accurately predict fatigue loads for both unwaked and waked turbines, encompassing scenarios of wake steering and induction control.

Nacelle modeling considerations for wind turbines using large-eddy simulations

Two setups are used to investigate differences between modeling a wind turbine nacelle by means of an actuator-line model (ALM) and a wall-model (WM) using large-eddy simulations. One advantage of the ALM is that it requires a lower mesh refinement, making it less computationally costly. In the first setup, the nacelle is in standalone configuration and the ALM results show a much lower turbulence intensity and a significantly slower wake recovery when compared to the WM cases. In the second setup, the nacelle is in a rotor-nacelle assembly configuration and many variations of the ALM are tested in order to match the results from the experiment addressed in the OC6 task phase III. Contrary to previous findings that the nacelle might affect the turbine loads, this study shows that the improved match with the experiment stems from the increased mesh refinement in the nacelle region rather than the actual presence of the nacelle. Nevertheless, the wake profiles in the near-wake show a very good agreement between the ALM and WM, regardless of the refinement in the nacelle region. These cases also show a higher wake deficit than not using any nacelle at all.

Comparative analysis of rolling contact fatigue life in a wind turbine pitch bearing with different modeling approaches

The load calculation for wind turbines usually does not take pitch bearings into account, and the pitch-bearing internal load distribution is calculated in a decoupled process. In the current study, a coupled wind turbine load simulation considering the pitch bearings is proposed. Internal pitch-bearing load distribution and roller contact fatigue life are compared to a decoupled approach using a high-fidelity finite-element model. Internal pitch-bearing load distribution can be an essential variable in almost all major pitch-bearing failures. Furthermore, rolling contact fatigue is one of the major types of pitch-bearing failure that can represent the bearing service life. The results show that the flexibility of the surroundings has an effect on the contact forces. As a result, the finite element model-based life calculation of the NREL 5 MW turbine predicts a longer rolling contact fatigue life for the bearing than the multi-body system-based one. For the IWT7.5, sample load cases indicate that this behavior is different, leading to the conclusion that the life of the bearing is significantly affected by the stiffness of the bearing and its surrounding components.

A semi-empirical model for time-domain tower Vortex induced Vibration load simulations of wind turbines

A new method for simulating aeroelastic resonances between vortex shedding and structural eigenfrequencies of a wind turbine tower is presented in this publication. This aero-elastic effect of Vortex induced Vibrations (ViV) has the potential to disrupt installation processes and cause fatigue and extreme loads on the tower. Despite this issue, there are no established simulation methods available in literature, able and sufficiently efficient to predict the ViV in turbulent and site specific design load conditions. This state-of-the-art is extended by introducing an efficient semi-empirical, time-domain simulation method, which is capable of analyzing the tower ViV excitations and aeroelastic responses in wind turbine load simulations of the isolated tower or the full turbine for fatigue and extreme loads. The method is based on an adaptive harmonic equation, which is automatically tuned to the instantaneous tower motions.

Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling

In the field of wind farm control, wake steering has shown the potential to increase the power output of a wind farm by deflecting wakes away from downstream turbines. However, in some wake steering scenarios, the fatigue damage experienced by the turbines can increase, particularly when the wakes partially overlap a downstream rotor. It is for this reason that fatigue load constraints should be introduced into the control optimisation process. Unfortunately, wind turbine loads are notoriously difficult to predict, requiring expensive aeroelastic simulations. In this study, we present a wind farm control optimisation with load constraints using surrogate models to estimate the fatigue damage of each turbine in a wind farm designed for maximum energy production. We use the state-of-the-art aeroelastic wind farm simulator, HAWC2Farm, to produce a comprehensive data set of fatigue loads, which is then used to train surrogate models for rapid execution during an optimisation loop. The inputs of the surrogate model are chosen using the most significant modes from a proper orthogonal decomposition. Artificial neural networks are used for the surrogate models, and the wind farm control optimisation is carried out using OpenMDAO. Finally, a wind farm control optimisation with load constraints using wake steering is performed. The presented methodology for surrogate modelling and control optimisation is significant to produce accurate set point optimisations for wind farms while recognising the implications to turbine fatigue loads.

Wind Turbine Loads in the Near Wake

This work is analyzing the the engineering wake models Frandsen and TNO for small inter-turbine distances. Therefore, loads generated by actuator line LES (AL-LES) wind fields will be compared with the loads generated by the mentioned engineering models. The results are checked for plausibility by comparing the current results with the available literature. Load comparisons for non-dimensional distances x/D ranging from 1.75 to 4 and for seven different wind speeds in order to consider the influence of the thrust coefficient were made. For each speed a low and a high turbulence intensity case is considered. Two latest generation turbines are investigated (one with a diameter of 138 m and one with a diameter of 175 m).Our studies reveal that the fatigue loads computed with the Frandsen and TNO model are conservative compared to the loads generated by AL-LES wind fields. The Wöhler averaged (m=10 and a sector of 120°) flap wise blade root bending moments are at least 150% higher for the TNO and Frandsen model compared to the loads generated by AL-LES wind fields for distances lower than 2.3D and C T values higher than 0.7. This indicates that current sector management rules applied by means of the results of the Frandsen and TNO model are too restrictive. A possible way to decrease the conservatism without posing at risk the structural integrity of the turbines is the following: The wake added turbulence intensity can be kept constant for regions in the parameter space with very high conservatism (e.g. for distances smaller than 2.3D and C T values higher than 0.7).

Mesoscale-coupled Large Eddy Simulation for Wind Resource Assessment

Turbulence, a key driver of wind turbine loads, is central in the assessment of turbine suitability and performance, and consequently impacts the expected energy production of a wind farm. Conventional flow modeling methods for wind resource assessment (WRA) typically lack the ability to resolve turbulence due to key simplifications in their formulation. This work applies Large Eddy Simulation (LES) to address these limitations and deliver high-fidelity wind condition predictions at 24 wind farm sites. The model includes recently improved boundary conditions to downscale information from global weather models via a mesoscale layer. Validation of the model is presented through comparison to over 100 years of measurements, with comparison of the time-series, distributions and spectra. Validation results indicate excellent performance of the model for key flow quantities including wind speed, turbulence and direction. Comparison of spectra indicates a significant improvement in the representation of the atmospheric energy cascade when compared to previous approaches. The results are significant for wind-farm site assessment as they demonstrate the feasibility of applying accurate LES at a commercial scale. LES also provides a step change in the quality of model predictions through resolving time and site-specific turbulence characteristics. The application of the model for WRA is a step towards improved understanding of the wind resource, as well as an improved suitability assessment process itself.

Control-oriented wind turbine load estimation based on local linear neuro-fuzzy models

To enhance energy production, the wind energy industry builds increasingly larger wind turbines (WT) in terms of hub height and rotor diameter. This enlargement results in higher structural loads, which emphasizes the importance of load-reducing WT control alongside the nominal control of power and rotational speed. Generally, it is not possible to directly measure these structural loads, so a controller needs to include their online estimation. Here we extend a baseline WT state estimator in the form of an Extended Kalman Filter (EKF) to include two unaccounted loads. These loads are a change of thrust in the main bearing and a yaw moment in the rotor hub. We incorporate these loads via data-driven local linear neuro-fuzzy models (LLNFM). These LLNFMs can represent nonlinear relationships while maintaining limited complexity. We use alaska/Wind to generate the underlying regression data and to perform the state estimation both in simulation. The regression data consists of nominal WT operation under turbulent conditions for different average wind speeds. We further superimpose the individual pitch angles to excite the WT. The evaluation is performed in a different scenario, using another turbulent wind field and a realistic noisy sensor configuration. While we can estimate the change of thrust in the one per revolution (1P) frequency range, we achieve more precise results for the yaw moment due to the incorporation of sensors with a strong correlation to it. The estimation of the change of thrust and the yaw moment appear to be sufficiently precise for load-reducing WT control in practically relevant frequency ranges.

Dynamics of the virtual center of wind pressure: An approach for the estimation of wind turbine loads

A new stochastic method for the reconstruction of time series of bending moments at the main shaft of a wind turbine is presented. The method is based on the characterization of the dynamics of the virtual center of wind pressure (CoWP). The CoWP, calculated purely from the incoming wind field, provides an approach for correlating the bending moments at the shaft with large-scale turbulent structures of the wind acting on the rotor plane. The proposed method allows a fast procedure for generating time series of loads on the shaft. As a result, the computational and time requirements for maximum and fatigue load assessment could be reduced.

Impact of dynamic stall model tailoring on wind turbine loads and performance prediction

The future of wind turbines will be characterised by long, slender blades subject to dynamic inflow and aeroelastic deflections. This makes the next generation of blades more prone to encounter dynamic stall effects, in which significant forces and loads fluctuations can be expected. Dynamic stall models can be tailored to suit the aerodynamics of different airfoils. Although different dynamic stall models exist, the impact of the choice of model, its implementation and calibration on the overall wind turbine performance remains to be assessed. In this work, we gathered an experimental dynamic dataset for a representative airfoil, the FFA-W3-211, to define the semi-empirical time constants for the Beddoes-Leishman dynamic stall model. An important differentiation is made between stall regions for positive and negative angles of attack, and the impact of tailored coefficients is assessed at airfoil scale. The difference between the tailored and untailored model is quantified for power performance and loads of the IEA 15 MW reference wind turbine. The results highlight a significant load over-prediction from the untailored Beddoes-Leishman model, whereas changes in power performance are negligible.

Time-Series Based Surrogate Model For Wind Farm Performance Prediction

Aeroelastic codes are state-of-the-art simulation tools in both industry and academia for the modelling of wind turbine loads and power output. Although these codes are widely used for the analysis of individual turbines, they are in general computationally too expensive for the calculation of all turbines within a wind farm. Engineering models that are computationally cheaper but also provide a lower fidelity are therefore typically used for wind farm power performance predictions. In this paper, an alternative approach to simulate wind farm performance is presented: the use of a data-driven surrogate model that is trained on time series that were generated by the in-house aeroelastic tool BHawC. This surrogate model provides results with potentially higher fidelity than more simplistic engineering models but is computationally much cheaper than BHawC simulations.

Integrating COAWST and OpenFAST for wind turbine loading

Modern research endeavors in wind energy have been increasingly focused on achieving accurate representations of wind turbine loading across diverse atmospheric conditions. Recent advancements in numerical weather prediction techniques make it possible to downscale weather conditions for operational use, underscoring the importance of including air-sea interactions using models such as the Coupled-Ocean-Atmosphere-Wave-Sediment Transport model to improve wind energy predictions. Nevertheless, challenges of high computational costs, the elusive ”gray zone” in simulations, and creating accurate wind turbine digital twins for predictive modeling remain.The current research addresses this challenge by proposing a novel modeling framework integrating the COAWST model with the OpenFAST aeroelastic solver. The current research strives to bridge the gap between the different dynamic regimes involved under diverse atmospheric conditions to achieve real-world representative wind turbine loading. Therefore, a scaled wind turbine model is implemented in the OpenFAST aeroelastic solver. Further, this research tests and discusses the proposed model framework’s capabilities and limitations under extreme weather phenomena.

Combining wake redirection and derating strategies in a load-constrained wind farm power maximization

Abstract. Power derating and wake redirection are two wind farm control techniques proposed in the last decade as means for increasing the overall wind farm power output. While derating operations are associated with a limited gain in terms of farm energy harvesting and with a decrease in turbine loading levels, farm controls based on wake redirection proved, both in silico and experimental tests, to entail significant increases in the overall wind farm power output. However, according to wake redirection strategies, the upstream wind turbines may typically operate at large yaw misalignment angles, and the possible increase in loads that the machines may experience in such conditions represents a source of concern when it comes to testing this control on existing farms that are not specifically designed for prolonged misaligned operations. In this work, it is first demonstrated that a suitable derating level can compensate for the increase in the rotor loads associated with large misalignment angles. Secondarily, two load-constrained wind farm controls based on a combination of wake redirection and derating are proposed with the aim of maximizing the overall farm output while maintaining unaltered design load envelope of the wind turbines operating within the controlled wind farm.