Dynamic Wake Meandering Research Articles

Low-frequency dynamic wake meandering: comparison of FAST.Farm and DIWA software tools

Most of the global dynamic response models used today for the design of wind turbines are based on the aero-hydro-servo-elastic analysis of one single turbine. However, research on bottom- fixed offshore wind turbines has shown that interactions among turbines in a farm influence both the power production and the structural loading. Furthermore, floating wind turbines (FWTs) are sensitive to low-frequency variations, and therefore to wake meandering perturbations. In the current work, we use the Dynamic Wake Meandering (DWM) model as implemented in DIWA and FAST.Farm, to study the low-frequency content of the meandering at a target turbine placed 8 diameters downstream. At frequencies in the range of the natural frequencies of rigid body motions of semisubmersible floaters, the two models yield different results. These differences are seen for every wind speed and wind turbine model, even though they decrease as the wind speed increases. The observed differences may affect low-frequency motions and consequently mooring system design.

Simplified wake modelling for wind farm load prediction

This paper presents a simple numerical wind farm model, where pragmatic choices are made in the modelling of underlying physical processes, with the aim of making useful power production and wind turbine load estimates. The numerical model decomposes the wind farm, inspired by the approach of the dynamic wake meandering model (DWM), into simple sub-models for a single wake deficit (1D Gaussian), wake meandering (statistical), and wake added turbulence (eddy viscosity based). Particular attention is given to selecting a momentum conserving wake summation method, because of its critical role in coupling the influence of individual wakes. Results are presented to illustrate the influence that wake summation methods have on equilibrium velocity and power production in a row of turbines, for different inter-turbine spacing and inflow velocities. Comparisons against published data from the Lillgrund wind farm illustrate that the suggested modelling approach reproduces important trends observed in the field data.

Validation of the dynamic wake meandering model with respect to loads and power production

Abstract. The outlined analysis validates the dynamic wake meandering (DWM) model based on loads and power production measured at an onshore wind farm with small turbine distances. Special focus is given to the performance of a version of the DWM model that was previously recalibrated at the site. The recalibration is based on measurements from a turbine nacelle-mounted lidar system. The different versions of the DWM model are compared to the commonly used Frandsen wake-added turbulence model. The results of the recalibrated wake model agree very well with the measurements, whereas the Frandsen model overestimates the loads drastically for short turbine distances. Furthermore, lidar measurements of the wind speed deficit as well as the wake meandering are incorporated in the DWM model definition in order to decrease the uncertainties.

Wake position tracking using dynamic wake meandering model and rotor loads

The wake position is crucial feedback information for closed loop wind farm control. This paper presents a wake center position tracking approach based on turbine rotor loads, which is feasible to track the wake position while the wake is impinging on the downstream wind turbine. The rotor equivalent wind characteristics are estimated based on the blade root bending moments. Furthermore, a wake-wind model is developed to describe the relationship between rotor averaged wind characteristics and wake position, in which the velocity deficit profile of the wake is defined by the Gaussian model. An adaptive extended Kalman filter is proposed to estimate the lateral and vertical wake position states, in which wake dynamics is described by the dynamic wake meandering model. The wake meandering is driven by a colored noise with a covariance matrix that is gain scheduled according to the rotor averaged wind characteristic. Uncertainties of wake position detection are discussed in various combinations of the ambient atmospheric condition. Simulation results show that the proposed tracking method achieves good estimates in low ambient turbulence intensity, while, for a wind condition with high turbulence intensity, relatively longer time averaging needs to be applied to smooth out short-term fluctuations and highlight longer-term wake trends.

FAST.Farm development and validation of structural load prediction against large eddy simulations

AbstractFAST.Farm is a mid‐fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on several principles of the dynamic wake meandering (DWM) model, but also addresses limitations of previous DWM implementations. Previous FAST.Farm studies have shown the similarities and differences between FAST.Farm and large eddy simulations for rigid turbine cases. The objective of this work is to quantify the ability of FAST.Farm to accurately predict turbine structural response in a small wind farm. This is done by comparing FAST.Farm structural response to SOWFA‐OpenFAST results for three laterally‐aligned turbines. The purpose of this study is to characterize the similarities and differences between FAST.Farm and a higher fidelity model for predicting turbine structural response in a wind farm for differing atmospheric inflows. Strong statistical agreement was found between FAST.Farm and SOWFA‐OpenFAST structural response for the non‐waked upstream turbine, and good agreement was found for the downstream turbines for most structural quantities. Higher differences were seen for downstream turbines with low ambient turbulence intensity or yawed turbines, suggesting areas for FAST.Farm wake dynamics modeling improvements. For all cases and turbines, small statistical differences were seen between blade deflections and bending moments, with larger differences for tower‐top and tower‐base bending moments. Overall, the results establish confidence for applying FAST.Farm to wind farm power and loads analyses and identify areas where further model validations and model improvements should be targeted.

Validation of FAST.Farm Against Full-Scale Turbine SCADA Data for a Small Wind Farm

FAST.Farm is a new midfidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on some of the principles of the Dynamic Wake Meandering model—including passive tracer modeling of wake meandering—but addresses many of the limitations of previous Dynamic Wake Meandering (DWM) implementations. Previous FAST.Farm verification studies show the similarities and differences between FAST.Farm and large-eddy simulations for rigid and flexible turbines. In this validation study, FAST.Farm turbine responses are compared to multiturbine measurements from a subset of a full-scale wind farm. FAST.Farm predictions of turbine generator power, rotor speed, and blade pitch for five-turbine simulations are compared to supervisory control and data acquisition results. Results reveal that FAST.Farm generator power mean and standard deviation results reasonably match measured data for upstream and downstream turbines, as well as the mean rotor speed and blade pitch above rated wind speeds. However, FAST.Farm generally underpredicts the mean rotor speed and overpredicts the mean blade pitch below rated operation. These errors are likely related to inaccuracies in the generic controller simulated. Despite controller differences, FAST.Farm predicts the same overall relative rotor power trends for all waked turbines at all wind speeds.

Effect of varying fidelity turbine models on wake loss prediction

Wind farm simulations are widely used in estimating energy yield and for optimal wind farm layout design. In the early design stages of a wind farm, low fidelity wind turbine models are used to estimate the farm power output, often due to incomplete knowledge of the turbine characteristics and the additional complexities. The discrepancies introduced in a wind farm simulation as a result of using low fidelity models can often be overlooked, leading to a misrepresentation of a wind farm’s yield. In this paper, the discrepancies between five levels of fidelity for two turbine designs are quantified, focusing on the produced wake and the downstream flow effects. Two high fidelity aeroelastic turbine models and three low fidelity models are described, where the wake is produced using the Dynamic Wake Meandering (DWM) model for each turbine and model. The results provide insight into the expected uncertainties in wake simulations as a result of changing the turbine fidelity level.

A Novel Wind Turbine Wake Steering Model Employing the Ainslie Velocity Deficit

Wind turbine wakes are an important source of production losses in wind parks, and strategies to mitigate these effects are being explored. One such strategy is yaw steering, where a wind turbine is purposefully misaligned with the wind to deflect its wake away from a downstream turbine, increasing overall farm power production. In order to optimize yaw steering control, fast and accurate models for wake deflection are necessary. Several models have been proposed in literature, often based on approximations of the wake shape. In the present work, a new model is proposed that computes wake deflection based on the result of the Ainslie wake deficit model used in the Dynamic Wake Meandering (DWM) approach that has recently entered the IEC61400 standard for wind turbines. The proposed formulation makes few additional assumptions and introduces no additional fitting parameters beyond those of the DWM wake deficit model. Results obtained using the new methodology are compared to two models from literature and to LES-ALM simulations in a limited number of cases, showing satisfactory results. More extensive comparisons in a variety of cases should be performed in order to better evaluate the proposed methodology. Possible future improvements to the presented formulation are put forward.

Extension of the DWM model towards a static model for site-specific load simulations

The outlined analysis presents a method to extend the Dynamic Wake Meandering (DWM) model to allow a combination with load approximation methods, which are commonly used to estimate the loads during the planning process of a new wind farm layout. Determining a wind farm layout is a highly iterative process, where time-consuming calculations are avoided as much as possible. The DWM model is methodically designed such that it delivers an inhomogeneous wind field, which makes it useless for further application in load approximation tools that generally require a single value for the turbulence intensity. Furthermore, the DWM model is developed in a way that turbulent wind fields need to be generated to define the ambient turbulence as well as the meandering of the wind speed deficit itself. Likewise, this time-consuming process of calculating a turbulent wind field is in strong contrast with iterative wind farm layout optimization processes. Therefore, the developed extension of the model uses a probability density function to describe the meandering and requires no wind field simulations. The static DWM model is finally compared to the commonly used Frandsen turbulence model as well as to the original DWM model and produces less conservative fatigue loads than the Frandsen model.

Dynamic wake meandering model calibration using nacelle-mounted lidar systems

Abstract. Light detection and ranging (lidar) systems have gained a great importance in today's wake characteristic measurements. The aim of this measurement campaign is to track the wake meandering and in a further step to validate the wind speed deficit in the meandering frame of reference (MFR) and in the fixed frame of reference using nacelle-mounted lidar measurements. Additionally, a comparison of the measured and the modeled wake degradation in the MFR was conducted. The simulations were done with two different versions of the dynamic wake meandering (DWM) model. These versions differ only in the description of the quasi-steady wake deficit. Based on the findings from the lidar measurements, the impact of the ambient turbulence intensity on the eddy viscosity definition in the quasi-steady deficit has been investigated and, subsequently, an improved correlation function has been determined, resulting in very good conformity between the new model and the measurements.

Snow-powered research on utility-scale wind turbine flows

This paper provides a review of the general experimental methodology of snow-powered flow visualization and super-large-scale particle imaging velocimetry (SLPIV), the corresponding field deployments and major scientific findings from our work on a 2.5 MW utility-scale wind turbine at the Eolos field station. The field measurements were conducted to investigate the incoming flow in the induction zone and the near-wake flows from different perspectives. It has been shown that these snow-powered measurements can provide sufficient spatiotemporal resolution and fields of view to characterize both qualitatively and quantitatively the incoming flow, all the major coherent structures generated by the turbine (e.g., blade, nacelle and tower vortices, etc.) as well as the development and interaction of these structures in the near wake. Our work has further revealed several interesting behaviors of near-wake flows (e.g., wake contraction, dynamic wake modulation, and meandering and deflection of nacelle wake, etc.), and their connections with constantly-changing inflows and turbine operation, which are uniquely associated with utility-scale turbines. These findings have demonstrated that the near wake flows, though highly complex, can be predicted with substantial statistical confidence using SCADA and structural response information readily available from the current utility-scale turbines. Such knowledge can be potentially incorporated into wake development models and turbine controllers for wind farm optimization in the future.

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg–Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model.

The effect of minimum thrust coefficient control strategy on power output and loads of a wind farm

The use of down-regulation control strategies for wind turbines potentially optimises the wind farm performance. One of the promising strategies to minimise the wind farm loads and maximise the energy production is by down-regulating the upstream turbine with minimum thrust coefficient (Ct ). Nonetheless, a majority of minimum Ct control studies is confined to fatigue load analysis of a single turbine. Therefore, this paper focuses on the investigation of the fatigue loads when operating turbines in a wind farm using a minimum Ct control strategy, as well as extreme loads and energy production. The control strategy is implemented in the DTU Wind Energy controller coupled with the aero-elastic code, HAWC2. The Dynamic Wake Meandering model in HAWC2 is used so that the wake deficits generated from the upstream turbines realistically represent the minimum Ct control strategy, as well as the interactions between wakes. The main contribution of this paper is to investigate the possible wind farm operating strategy using minimum Ct control that leads to decrease the lifetime fatigue loads and increase the overall energy production of a wind farm due to the potential extended lifetime of certain wind turbine components.

A Review on the Meandering of Wind Turbine Wakes

Meandering describes the large-scale, low frequency motions of wind turbine wakes, which could determine wake recovery rates, impact the loads exerted on turbine structures, and play a critical role in the design and optimal control of wind farms. This paper presents a comprehensive review of previous work related to wake meandering. Emphasis is placed on the origin and characteristics of wake meandering and computational models, including both the dynamic wake meandering models and large-eddy simulation approaches. Future research directions in the field are also discussed.

Effect of Wake Meandering on Aeroelastic Response of a Wind Turbine Placed in a Park

A wind turbine operating inside a wind farm is subjected to increased turbulence intensity and reduced wind speeds resulting in increased fatigue loadings and reduced power production. Furthermore, meandering of the wakes results in increased dynamic loading of the wind turbine. In the present study, a standalone dynamic wake meandering (DWM) model has been developed and implemented in commercial code SIMA. The standalone DIWA model does not require a direct coupling with aeroelastic code, hence it is computationally fast. Although the standalone tool is a good alternative for power and thrust prediction, it does not have the capability to predict the turbine aeroelastic loads. The new DWM program is referred as “Disturbed Inflow Wind Analyzer” (DIWA). Benchmarking studies of DIWA with the literature data are presented and discussed. Overall the DIWA compares well with the literature data and the discrepancies between DIWA and the literature data are discussed. The present studies show that the wake deficit profiles are very sensitive to the eddy viscosity parameters. Finally, the turbulence boxes generated using DIWA have been used for understanding the aeroelastic behaviour of NREL 5MW turbine and one of the wind turbines from the Lillgrund wind park. The estimated power production using both aeroelastic coupled with DIWA turbulence boxes and standalone DIWA (without aeroelastic) are in good agreement with the literature data. The trends of fatigue loads are predicted well, with a few exceptions.

Surrogate models for parameterized representation of wake‐induced loads in wind farms

AbstractA procedure for mapping wake‐induced load predictions computed with the dynamic wake meandering model to a computationally efficient surrogate model approximation is defined and demonstrated. Using the mapping function, the load variation can be efficiently estimated for a wind farm with arbitrary layout. The resulting load assessment procedure provides continuous, differentiable output with known analytical derivatives and can be used for applications such as wind turbine layout optimization, estimation of turbine lifetime, and uncertainty analysis.

Effects of Inflow Spatiotemporal Discretization on Wake Meandering and Turbine Structural Response using FAST.Farm

FAST.Farm is a newly developed multiphysics, midfidelity engineering tool that can be used to predict turbine power and structural loads of wind turbines in a wind farm with minimal computational expense. Previous studies have shown similarities and differences in wind turbine performance between FAST.Farm and high-fidelity large-eddy simulations (LES) using both LES precursor-generated inflow and TurbSim-generated synthetic inflow. While conservative resolutions have been used to date, no formal spatial or temporal discretization study has been performed for the FAST.Farm model wind domains. This work aims to study the effects of varying the spatial and temporal discretization of the wind domains on wake meandering (low-resolution domain) and turbine structural response (high-resolution domains) and resulting wake and load calculations. The purpose of this study is to establish convergence criteria and recommendations for discretization values in terms of rotor diameter (D, expressed in meters) that will maximize computational efficiency. To ensure a percent error of ≤ 1% in standard deviation of wake center position relative to the finest included resolution, a low-resolution time step of 0.024D s, 0.016D s, and 0.0079D s and a low-resolution spatial discretization of 0.079D m, 0.16D m, and 0.24D m is recommended for 8 m/s, 12 m/s, and 18 m/s mean ambient wind speed at hub height, respectively. These guidelines are likely applicable to other implementations of the dynamic wake meandering model. To ensure a percent error of ≤ 1% in standard deviation of all considered structural response outputs, a high-resolution time step that captures the highest influential excitation and natural frequencies in the system and a high-resolution spatial discretization comparable to the maximum airfoil cord length is recommended. These guidelines are likely applicable to any aeroelastic analysis.

Optimal yaw strategy for optimized power and load in various wake situations

The interaction between nearby wind turbines in a wind farm modifies the power and loads compared to their stand-alone values. The increased turbulence intensity and the modified turbulence structure at the downstream turbines creates higher fatigue loading, which can be mitigated by wind farm and/or wind turbine control. To alleviate loads and maximize power possible strategies such as wake steering, where the turbine is yawed to redirect the wake such that it does not impinge the downstream turbine, have been studied. The work presented here focuses on situations where the wake is nevertheless affecting the downstream turbine, and more specifically how high loads can be avoided by yawing the wake-affected turbine. The analysis is conducted on a 2.3 MW machine, and the flow field is simulated using the Dynamic Wake Meandering model. The study investigates the impact on power and loads for different longitudinal interspacing and turbulence intensities. Optimal yaw strategies are defined for above rated regions where no power loss occurs. The potential load alleviation for different load sensors are studied, but the presentation is focussed on the blade root flapwise fatigue loading. For full wake at 3D interspacing and turbulence intensity of 5 %, around 35 % of load reduction on the 1 Hz Damage Equivalent Loads can be achieved at high wind speeds. Smaller reductions are achieved for higher atmospheric turbulence; the analogue case with 15 % turbulence intensity shows 17 % potential alleviation. The alleviation on the wind turbine lifetime is also calculated and compared for different turbulence intensities and mean wind speeds. Small reductions are achieved for sites with low mean wind speed and high turbulence intensity, but high reductions, of around 19 %, are accomplished in low turbulence intensity with high mean wind speed.

Engineering an optimal wind farm using surrogate models

AbstractA framework for optimal design of wind farm layouts using a surrogate‐based Dynamic Wake Meandering model is presented. The optimization platform is set‐up as a hybrid strategy combining genetic search with the gradient‐based algorithm. The design variables are the number of turbines in the layout and their relative position within the bounded area. The objective function is defined as the net present value of the wind farm's profit, thus including the relevant expenditures throughout the farm's lifespan. Results show that an optimal design is reached by maximizing investment and accepting a minor sacrifice of the wind farm performance.

Validation of wind turbine wake models with focus on the dynamic wake meandering model

This analysis compares current wake models, such as the Sten Frandsen turbulence model, an engineering wind speed deficit and turbulence model developed by G. Chr. Larsen, the N. O. Jensen wind speed deficit description and three variations of the dynamic wake meandering (DWM) model to measurements from onshore wind farms. Special attention is given to the dynamic wake meandering model. Today different versions or implementations of the dynamic wake meandering model exist. These versions differ among others in the calculation of the quasi-steady wake deficit in the meandering frame of reference. The influence of these calculation methods on loads and yield is analysed in this work. The validation of the mentioned wake models is based on turbine load, power and wind measurements from two onshore wind farms. One key result of this work is that the DWM model in most cases coincides superiorly with the measurement results compared to the other engineering wake models commonly used in industry.