Wind Farm Flow Research Articles

Large eddy simulation with realistic geophysical inflow of Alpha Ventus wind farm: a comparison with LIDAR field experiments

In the current paper we perform a high Reynolds number large eddy simulation (LES) study of the offshore Alpha Ventus wind farm in North Sea. Actuator line modeling has been used for the rotating turbines. The wind farm is driven by a neutral atmospheric boundary layer coupled with realistically varying mean wind flux and direction from cup and vane anemometer data. We carry out comparison of LES and LIDAR based experimental results obtained from low-elevation angle Plan Position Indicator (PPI) scans of the velocity structures between two turbines. The results indicate towards a more fundamental understanding on the capability of LES inflow methodology in capturing realistic large scale structures that are observed in wind farm flows.

Data-driven Wake Modelling for Reduced Uncertainties in short-term Possible Power Estimation

One of the ancillary services the wind farms are required to provide to the system operators is reserve power, which is achieved by down-regulating the wind farm from its possible power. In order to estimate the reserves, the possible power needs to be calculated by correcting the reduced wake effects behind the down-regulated turbines. The most recent grid codes dictate the quality of the possible power at the wind farm level to be assessed within 1-min intervals for offshore wind power plants. Therefore, the necessity of a fast and reliable wake model is more prominent than ever.Here we investigate the performance of two engineering wake models with 1-sec resolution SCADA data on three different offshore wind farms, given the quantified input uncertainty. The preliminary results show that, even wind farm specific training of the model parameters might fail to comply with the strict criteria stated in the grid codes, especially for the layouts with significant wake losses. In order to tackle the inadequacy of the engineering wake models to capture some of the dynamics in the wind farm flow due to the embedded assumptions, purely data-driven techniques are evaluated. The flexibility of such an on-line model enables ‘site-turbine-time-specific’ modelling, in which the parameters are defined per turbine and updated with each time-step in a specific wind farm.

3-D shear-layer model for the simulation of multiple wind turbine wakes: description and first assessment

Abstract. The number of turbines installed in offshore wind farms has strongly increased in the last years and at the same time the need for more precise estimations of the wind farm efficiency too. In this sense, the interaction between wakes has become a relevant aspect for the definition of a wind farm layout, for the assessment of its annual energy yield and for the evaluation of wind turbine fatigue loads. For this reason, accurate models for multiple overlapping wakes are a main concern of the wind energy community. Existing engineering models can only simulate single wakes, which are superimposed when they are interacting in a wind farm. This method is a practical solution, but it is not fully supported by a physical background. The limitation to single wakes is given by the assumption that the wake is axisymmetric. As an alternative, we propose a new shear-layer model that is based on the existing engineering wake models but is extended to also simulate non-axisymmetric wakes. In this paper, we present the theoretical background of the model and four application cases. We evaluate the new model for the simulation of single and multiple wakes using large-eddy simulations as reference. In particular, we report the improvements of the new model predictions in comparison to a sum-of-squares superposition approach for the simulation of three interacting wakes. The lower deviation from the reference considering single and multiple wakes encourages the further development of the model and promises a successful application for the simulation of wind farm flows.

Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm

The mesoscale Weather Research and Forecasting (WRF) model coupled with wind farm parameterization is newly developed to simulate the wake flow and power production of a real onshore wind farm. First, wind farm flow field simulations are conducted with 1000m, 500m and 200m horizontal resolutions, and the simulation results capture the wind farm observed data well. In addition, wind farm flow characteristics, power output, and influence on the atmosphere boundary layer (ABL) are resolved at a horizontal resolution of 200m. The wake interactions, wind speed, and power output deficit in the wind farm are analyzed. The power comparison results prove that the proposed method can be applied to simulate the power output of a real onshore wind farm with high accuracy in real time. The influence of the wind farm on the ABL is also discussed. The results show that wind farm effects on the ABL occur mainly within the turbine rotor-spanned heights and the downstream regions behind the wind farm within 10km, within which the speed deficit ratio can exceed 10%. For the region that is 18km downstream of the wind farm, the average speed deficit ratio is only about 2%. This study is the first attempt to reproduce the wake flow and power output of a real onshore wind farm by the WRF model at such high resolution.

Analytical Model for Mean Flow and Fluxes of Momentum and Energy in Very Large Wind Farms

As wind-turbine arrays continue to be installed and the array size continues to grow, there is an increasing need to represent very large wind-turbine arrays in numerical weather prediction models, for wind-farm optimization, and for environmental assessment. We propose a simple analytical model for boundary-layer flow in fully-developed wind-turbine arrays, based on the concept of sparsely-obstructed shear flows. In describing the vertical distribution of the mean wind speed and shear stress within wind farms, our model estimates the mean kinetic energy harvested from the atmospheric boundary layer, and determines the partitioning between the wind power captured by the wind turbines and that absorbed by the underlying land or water. A length scale based on the turbine geometry, spacing, and performance characteristics, is able to estimate the asymptotic limit for the fully-developed flow through wind-turbine arrays, and thereby determine if the wind-farm flow is fully developed for very large turbine arrays. Our model is validated using data collected in controlled wind-tunnel experiments, and its usefulness for the prediction of wind-farm performance and optimization of turbine-array spacing are described. Our model may also be useful for assessing the extent to which the extraction of wind power affects the land–atmosphere coupling or air–water exchange of momentum, with implications for the transport of heat, moisture, trace gases such as carbon dioxide, methane, and nitrous oxide, and ecologically important oxygen.

A New Miniature Wind Turbine for Wind Tunnel Experiments. Part I: Design and Performance

Miniature wind turbines, employed in wind tunnel experiments to study the interaction of turbines with turbulent boundary layers, usually suffer from poor performance with respect to their large-scale counterparts in the field. Moreover, although wakes of wind turbines have been extensively examined in wind tunnel studies, the proper characterization of the performance of wind turbines has received relatively less attention. In this regard, the present study concerns the design and the performance analysis of a new three-bladed horizontal-axis miniature wind turbine with a rotor diameter of 15 cm. Due to its small size, this turbine, called WiRE-01, is particularly suitable for studies of wind farm flows and the interaction of the turbine with an incoming boundary-layer flow. Especial emphasis was placed on the accurate measurement of the mechanical power extracted by the miniature turbine from the incoming wind. In order to do so, a new setup was developed to directly measure the torque of the rotor shaft. Moreover, to provide a better understanding on the connection between the mechanical and electrical aspects of miniature wind turbines, the performance of three different direct-current (DC) generators was studied. It is found that electrical outputs of the tested generators can be used to provide a rather acceptable estimation of the mechanical input power. Force and power measurements showed that the thrust and power coefficients of the miniature turbine can reach 0.8 and 0.4 , respectively, which are close to the ones of large-scale turbines in the field. In Part II of this study, the wake structure and dynamic flow characteristics are studied for the new miniature turbine immersed in a turbulent boundary-layer flow.

Dynamic Flow Model for Real-Time Application in Wind Farm Control

For short-term power predictions and estimations of the available power during curtailment of a wind farm, it is necessary to consider the flow dynamics and aerodynamic interactions of the turbines. In this paper, a control-oriented dynamic two-dimensional wind farm model is introduced that aims to incorporate real-time measurements such as flow velocities at turbine locations to estimate the ambient wind farm flow. The model is intended to derive flow predictions for real-time applications. Since fully resolved computational fluid dynamics are too CPU-intensive for such a task, the dynamic model presented in this paper relies on an approximation of the flow equations in a two-dimensional framework. A semi-Lagrangian advection scheme and a step-wise flow solver together offer fast calculation speed, which scales linearly with the number of grid points. In order to emulate effects of realistic three-dimensional wind farm flow, a relaxation of the two-dimensional continuity equation is presented. Furthermore, with little extra computational expense, additional dynamic state variables for various possible applications can be propagated along the wind flow. For instance, a dynamic confidence parameter can provide estimations of the accuracy of flow predictions, while a turbulence parameter adds the possibility to estimate wake induced loads on downstream turbines. In order to demonstrate the performance and validity of the new model it is compared with other models. At first a two turbine reference case is compared with a steady-state model and secondly with results obtained by the dynamic wind farm flow model WFSim. Finally a small wind farm is simulated in order to show the computational scaling of the model.

A survey of modelling methods for high-fidelity wind farm simulations using large eddy simulation.

Large eddy simulations (LES) of wind farms have the capability to provide valuable and detailed information about the dynamics of wind turbine wakes. For this reason, their use within the wind energy research community is on the rise, spurring the development of new models and methods. This review surveys the most common schemes available to model the rotor, atmospheric conditions and terrain effects within current state-of-the-art LES codes, of which an overview is provided. A summary of the experimental research data available for validation of LES codes within the context of single and multiple wake situations is also supplied. Some typical results for wind turbine and wind farm flows are presented to illustrate best practices for carrying out high-fidelity LES of wind farms under various atmospheric and terrain conditions.This article is part of the themed issue 'Wind energy in complex terrains'.

Boundary-layer development and gravity waves in conventionally neutral wind farms

While neutral atmospheric boundary layers are rare over land, they occur frequently over sea. In these cases they are almost always of the conventionally neutral type, in which the neutral boundary layer is capped by a strong inversion layer and a stably stratified atmosphere aloft. In the current study, we use large-eddy simulations (LES) to investigate the interaction between a large wind farm that has a fetch of 15 km and a conventionally neutral boundary layer (CNBL) in typical offshore conditions. At the domain inlet, we consider three different equilibrium CNBLs with heights of approximately 300 m, 500 m and 1000 m that are generated in a separate precursor LES. We find that the height of the inflow boundary layer has a significant impact on the wind farm flow development. First of all, above the farm, an internal boundary layer develops that interacts downwind with the capping inversion for the two lowest CNBL cases. Secondly, the upward displacement of the boundary layer by flow deceleration in the wind farm excites gravity waves in the inversion layer and the free atmosphere above. For the lower CNBL cases, these waves induce significant pressure gradients in the farm (both favourable and unfavourable depending on location and case). A detailed energy budget analysis in the turbine region shows that energy extracted by the wind turbines comes both from flow deceleration and from vertical turbulent entrainment. Though turbulent transport dominates near the end of the farm, flow deceleration remains significant, i.e. up to 35 % of the turbulent flux for the lowest CNBL case. In fact, while the turbulent fluxes are fully developed after eight turbine rows, the mean flow does not reach a stationary regime. A further energy budget analysis over the rest of the CNBL reveals that all energy available at turbine level comes from upwind kinetic energy in the boundary layer. In the lower CNBL cases, the pressure field induced by gravity waves plays an important role in redistributing this energy throughout the farm. Overall, in all cases entrainment at the capping inversion is negligible, and also the work done by the mean background pressure gradient, arising from the geostrophic balance in the free atmosphere, is small.

Analysing wind turbine fatigue load prediction: The impact of wind farm flow conditions

Lifetime evaluation with fatigue loads is commonly used in the design phase of wind turbines, but rarely during operation due to the cost of extra measurements. Fatigue load prediction with neural networks, using existing SCADA signals, is a potential cost-effective alternative to continuously monitor lifetime consumption. However, although assessments for turbines in wind farm flow have been pointed out as deficient, the evaluations were limited to single cases and the implication for the design of a monitoring system was not discussed. Hence, we proposed metrics to evaluate prediction quality and, using one year of measurements at two wind turbines, we evaluated predictions in six different flow conditions. The quality of fatigue load predictions were evaluated for bending moments of two blades, in edgewise and flapwise directions. Results, based on 48 analyses, demonstrated that prediction quality varies marginally with varying flow conditions. Predictions were accurate in all cases and had an average error below 1.5%, but their precision slightly deteriorated in wake flow conditions. In general, results demonstrated that a reasonable monitoring system can be based on a neural network model without the need to distinguish between inflow conditions.

A linearized numerical model of wind-farm flows

A fast and reasonably accurate numerical three-dimensional wake model able to predict the flow behaviour of a wind farm over a flat terrain has been developed. The model is based on the boundary-la ...

Enhanced Kalman Filtering for a 2D CFD NS Wind Farm Flow Model

Wind turbines are often grouped together for financial reasons, but due to wake development this usually results in decreased turbine lifetimes and power capture, and thereby an increased levelized cost of energy (LCOE). Wind farm control aims to minimize this cost by operating turbines at their optimal control settings. Most state-of-the-art control algorithms are open-loop and rely on low fidelity, static flow models. Closed-loop control relying on a dynamic model and state observer has real potential to further decrease wind's LCOE, but is often too computationally expensive for practical use. In this paper two time-efficient Kalman filter (KF) variants are outlined incorporating the medium fidelity, dynamic flow model “WindFarmSimulator” (WFSim). This model relies on a discretized set of Navier-Stokes equations in two dimensions to predict the flow in wind farms at low computational cost. The filters implemented are an Ensemble KF and an Approximate KF. Simulations in which a high fidelity simulation model represents the true wind farm show that these filters are 101 —102 times faster than a regular KF with comparable or better performance, correcting for wake dynamics that are not modeled in WFSim (noticeably, wake meandering and turbine hub effects). This is a first big step towards real-time closed-loop control for wind farms.

Mesoscale to microscale wind farm flow modeling and evaluation

The increasing size of wind turbines, with rotors already spanning more than 150 m diameter and hub heights above 100 m, requires proper modeling of the atmospheric boundary layer (ABL) from the surface to the free atmosphere. Furthermore, large wind farm arrays create their own boundary layer structure with unique physics. This poses significant challenges to traditional wind engineering models that rely on surface‐layer theories and engineering wind farm models to simulate the flow in and around wind farms. However, adopting an ABL approach offers the opportunity to better integrate wind farm design tools and meteorological models. The challenge is how to build the bridge between atmospheric and wind engineering model communities and how to establish a comprehensive evaluation process that identifies relevant physical phenomena for wind energy applications with modeling and experimental requirements. A framework for model verification, validation, and uncertainty quantification is established to guide this process by a systematic evaluation of the modeling system at increasing levels of complexity. In terms of atmospheric physics, ‘building the bridge’ means developing models for the so‐called ‘terra incognita,’ a term used to designate the turbulent scales that transition from mesoscale to microscale. This range of scales within atmospheric research deals with the transition from parameterized to resolved turbulence and the improvement of surface boundary‐layer parameterizations. The coupling of meteorological and wind engineering flow models and the definition of a formal model evaluation methodology, is a strong area of research for the next generation of wind conditions assessment and wind farm and wind turbine design tools. Some fundamental challenges are identified in order to guide future research in this area. WIREs Energy Environ 2017, 6:e214. doi: 10.1002/wene.214This article is categorized under: Wind Power > Climate and Environment Energy and Climate > Climate and Environment Energy Policy and Planning > Climate and Environment

Coalescing Wind Turbine Wakes

A team of researchers from the National Renewable Energy Laboratory and Statoil used large-eddy simulations to numerically investigate the merging wakes from upstream offshore wind turbines. Merging wakes are typical phenomena in wind farm flows in which neighboring turbine wakes consolidate to form complex flow patterns that are as yet not well understood. In the present study, three 6-MW turbines in a row were subjected to a neutrally stable atmospheric boundary layer flow. As a result, the wake from the farthest upstream turbine conjoined the downstream wake, which significantly altered the subsequent velocity deficit structures, turbulence intensity, and the global meandering behavior. The complexity increased even more when the combined wakes from the two upstream turbines mixed with the wake generated by the last turbine, thereby forming a “triplet” structure. Although the influence of the wake generated by the first turbine decayed with downstream distance, the mutated wakes from the second turbine continued to influence the downstream wake. Two mirror-image angles of wind directions that yielded partial wakes impinging on the downstream turbines yielded asymmetric wake profiles that could be attributed to the changing flow directions in the rotor plane induced by the Coriolis force. The turbine wakes persisted for extended distances in the present study, which is a result of low aerodynamic surface roughness typically found in offshore conditions.

Offshore wind farm flow measured by complementary remote sensing techniques: radar satellite TerraSAR-X and lidar windscanners

Scanning Doppler lidar systems offer continuous wind measurements with some kilometres of range and a spatial distribution of concurrent measurements down to some metres. The synthetic aperture radar (SAR) satellite TerraSAR-X is capable to cover offshore areas of hundreds of square kilometres and to obtain wind data spatially distributed with some tens of metres. Images can be taken up to twice a day when the satellite passes the measurement site.Simultaneous wind speed measurements with ground based scanning Doppler lidar and TerraSAR-X in the region of the offshore wind farm ”alpha ventus” in the German North Sea were collected. A comparison of both systems in free stream conditions is performed by extrapolating the lidardata to the measurement height of the radar satellite assuming a logarithmic wind profile. In wake conditions the wake tracks obtained by lidar and TerraSAR-X are compared. In free stream conditions the comparison reveals a mean absolute wind velocity difference ≤ 0.4 m/s in two of the four considered cases and 1.1 m/s in one case. The fourth case shows a bad agreement due to a unusually low radar backscatter in the satellite's measurement. In wake conditions the wind turbine wakes could be tracked in the lidar and the satellite data. The comparison for the considered case reveals similar wake tracks in principle, but no matching due to the time difference of the measurements and the lower spatial resolution of the radar measurements.

First comparison of LES of an offshore wind turbine wake with dual-Doppler lidar measurements in a German offshore wind farm

Large-Eddy Simulations (LES) are more and more used for simulating wind turbine wakes as they resolve the atmospheric as well as the wake turbulence. Considering the expenses and sparsity of offshore measurements, LES can provide valuable insights into the flow field in offshore wind farms. However, for an application of LES wind fields to assess offshore wind farm flow, a proper validation with measured data is necessary. Such a proper validation requires that the LES can closely reproduce the atmospheric conditions during the measurement. For this purpose, a representation of the large-scale features that drive the wind flow is required. Large-scale-forcing and nudging of the LES model PALM is tested with reanalysis data of the COSMO-DE model for a case study during one particular day in the beginning of 2014 at a German offshore wind farm. As wind and temperature profiles of the LES prove to follow the large-scale features closely, the wake of a single wind turbine is simulated with an advanced version of an actuator disc model. Measurement data is provided by processed dual-Doppler lidar measurements during the same day in the same wind farm. Several methods have been investigated at the University of Oldenburg to compare LES wind fields and lidar measurements. In this study a dual-Doppler algorithm was applied in order to estimate the horizontal stationary wind field. The raw data originate from Plan Position Indicator (PPI) measurements, which have been performed with two long-range wind lidars installed at different opposing platforms at the border of the wind farm.

How Many Model Evaluations Are Required To Predict The AEP Of A Wind Power Plant?

Wind farm flow models have advanced considerably with the use of large eddy simulations (LES) and Reynolds averaged Navier-Stokes (RANS) computations. The main limitation of these techniques is their high computational time requirements; which makes their use for wind farm annual energy production (AEP) predictions expensive. The objective of the present paper is to minimize the number of model evaluations required to capture the wind power plant's AEP using stationary wind farm flow models. Polynomial chaos techniques are proposed based on arbitrary Weibull distributed wind speed and Von Misses distributed wind direction. The correlation between wind direction and wind speed are captured by defining Weibull-parameters as functions of wind direction. In order to evaluate the accuracy of these methods the expectation and variance of the wind farm power distributions are compared against the traditional binning method with trapezoidal and Simpson's integration rules.The wind farm flow model used in this study is the semi-empirical wake model developed by Larsen [1]. Three test cases are studied: a single turbine, a simple and a real offshore wind power plant. A reduced number of model evaluations for a general wind power plant is proposed based on the convergence of the present method for each case.

A Fast Reduced Order Method for Assessment of Wind Farm Layouts

Due to wake losses, the power production of wind farms depend on the positioning of turbines in relation to each other, i.e. the wind farm layout. Computational Fluid Dynamics (CFD) can be used for solving the complex flow in a wind farm since wake effects are inherently simulated. However, it is too slow for interactively assessing the power production for variations of wind farm layouts. Therefore, there is a need for an accurate and fast model based on CFD for computing the flow field and the wake losses in a wind farm for a range of wind speeds and wind directions. In this paper, we adapt model reduction methodology to wind farm flow. The governing equations are formulated in a reduced finite dimensional space spanned by a set of orthogonal modes created from CFD simulations. The accuracy of the reduced model is assessed on a set of test cases. In the reduced model, new solutions for different turbine layouts and wind speeds are produced in a matter of seconds, as opposed to hours for full CFD simulations.

Equivalent Modeling of Double-fed Wind Farm Based on Flow Field Characteristics

The entire wind farm is regarded as a large-capacity wind turbine for modeling double-fed wind farm. The influence of flow field characteristics in the wind farm has been ignored. In order to reflect the influence in double-fed wind farm equivalent modeling, an equivalent modeling method of double-fed wind farm based on the flow field characteristics has been proposed. A calculation model of the double-fed wind farm flow field has been built considering the influence of terrain and wake. The wind speed was calculated at each wind turbine installed point based on the flow field calculation model, and using the wind speed as the input wind speed for each wind turbine, the dynamic characteristics of the wind farm production process has been studied. The simulation analysis has been done for the proposed modeling method, and the simulation results show that the proposed modeling method can reflects the dynamic characteristics of the wind farm production process more accurately.

IEA-Task 31 WAKEBENCH: Towards a protocol for wind farm flow model evaluation. Part 2: Wind farm wake models

Researchers within the International Energy Agency (IEA) Task 31: Wakebench have created a framework for the evaluation of wind farm flow models operating at the microscale level. The framework consists of a model evaluation protocol integrated with a web-based portal for model benchmarking (www.windbench.net). This paper provides an overview of the building-block validation approach applied to wind farm wake models, including best practices for the benchmarking and data processing procedures for validation datasets from wind farm SCADA and meteorological databases. A hierarchy of test cases has been proposed for wake model evaluation, from similarity theory of the axisymmetric wake and idealized infinite wind farm, to single-wake wind tunnel (UMN-EPFL) and field experiments (Sexbierum), to wind farm arrays in offshore (Horns Rev, Lillgrund) and complex terrain conditions (San Gregorio). A summary of results from the axisymmetric wake, Sexbierum, Horns Rev and Lillgrund benchmarks are used to discuss the state-of-the-art of wake model validation and highlight the most relevant issues for future development.