Wake Effects Research Articles

Synchronized Dynamic Induction Control: An Experimental Investigation

Wind turbines in farms face challenges such as reduced power output and increased loading when their rows align with the wind direction—a phenomenon known as the wake effect. To address this issue, dynamic induction control has been proposed, which involves dynamically adjusting the induction of upstream turbines to enhance the mixing of the wake with the free stream. As a continuation of this method, downstream turbines could potentially leverage the periodic structure in the upstream turbines’ wake to improve power production further downstream by synchronizing their dynamic induction control actions. This study investigates the potential of such an approach using a three-turbine scaled setup in a wind tunnel. The findings reveal that synchronization not only improves wake mixing downstream but also results in a substantial power gain on the synchronizing turbine, suggesting potential for a synchronization controller.

Coupled modeling of wake steering and platform offsets for floating wind arrays

Wake effects are a key challenge in the design and analysis of wind farms. For floating wind farms, the platforms offset under the aerodynamic loading of the turbine and are constrained by mooring systems that can vary significantly in allowable offsets. When considering wake steering, the crosswind offset of the turbine can counteract the lateral deflection of the wake. This work presents a tool to efficiently model the coupled impacts of wake steering and platform offsets for floating wind farms. The tool relies on the frequency-domain wind farm model RAFT and the steady-state wake model FLORIS. A verification with FAST.Farm is presented, then the tool is applied to a simple two-turbine case study. A range of mooring systems with increasing platform offsets and varied yaw misalignment angles are considered while comparing the impact on turbine power. Additional sensitivities to turbine spacing and mooring system orientation are explored. The results show that there is a least-optimal watch circle width for downwind turbine power production that varies with yaw misalignment angle and turbine spacing. Additionally, the turbine offsets under yaw-misaligned conditions vary significantly depending on mooring system orientation relative to the rotor plane, which in turn impacts the optimal misalignment angle. These results highlight the importance of including floating platform offsets and mooring systems in the evaluation of wake steering strategies for floating wind arrays.

Passive Mooring-based Turbine Repositioning Technique for Wake Steering in Floating Offshore Wind Farms

Power loss due to wake effects from upstream wind turbines is an important factor in the design of floating wind farms. These wake disturbances also increase fatigue loads on downstream units. Economic-driven wind farm layout optimization may culminate in non-standard, irregular configurations of offshore wind farms that deviate from conventional engineering practices. Consequently, wind farm developers are inclined towards a more geometrically coherent layout that are sub-optimal. Floating wind turbines horizontal offsets can be used to overcome wake losses. In this study, a novel method suggesting various mooring orientation methods to passively reposition the wind turbines in the farm to maximize annual energy yield. Preliminary parametric optimization demonstrates the effectiveness of the proposed method for standard floating wind farm designs to reduce wake effects by as much as 30% at rated wind speed compared to a baseline case, while preserving the farm’s overall uniformity.

Multidisciplinary Wind Farm Electrical Infrastructure Optimization

During the construction stage of a wind farm, it is paramount to consider which layouts are the most efficient. Recent literature has found that optimization of the annual energy production is not necessarily driven by the same factors as other economic metrics, such as the internal rate of return. One parameter that makes this particularly true is the electrical cable length and layout, which affect both the cash flow of the project and the investments. While placing turbines far from each other is beneficial to mitigate the wake effect, the capital expenditures associated with the longer cables affects economic metrics. On the other hand, even though placing turbines close to each other reduces cable expenses, wind turbine mechanical loads are challenging to determine from wake effects. This work investigates trends between annual energy production, internal rate of return, electrical cable layout, and turbine components degradation due to mechanical loads. Results point towards corroborating recent literature, finding financial opportunities to explore tighter layouts with shorter electrical cables.

Wake Effect Quantification using SCADA Data and LES Modelling of an Operational Offshore Wind Farm

Wake effects in the Anholt offshore wind farm have been investigated using both operational data and a Large Eddy Simulation (LES) model of a group of five turbines within the wind farm. Analysis of operational data showed that the variations of main shaft speeds of the downstream turbines were almost six times those of the upstream turbine at near-rated operation. The aim of the LES was to study the impact of atmospheric stability on the wind turbine array performance and compare this with the field data. An LES precursor method was used to model the near-neutral and unstably stratified atmospheric boundary layers that represent typical conditions in winter and summer, respectively, and the turbines in wind farm model were simulated using an actuator line method. It was found that LES with the actuator line method and generic turbine design data can generate a reasonable mean power generation trend for the Anholt wind farm under near-neutral and unstable conditions. The maximum difference in the mean power output between the LES and averaged operational data was approximately 20%.

Assessing the impacts of wakes on floating wind farms with shared anchors

This paper examines wake effects for floating wind farms with shared anchors. Three 20-turbine farms are examined: a grid-formation baseline with no shared anchors, a farm based on 3-line anchors, and a farm based on 6-line anchors, governed by a wind turbine spacing of 8 rotor diameters. The IEA 15 MW reference turbine on the UMaine semisubmersible platform was used with a taut mooring system in deep- water depths representative of U.S. west coast lease areas. A steady-state wake model showed that, when evaluating a sweep of wind headings, the baseline design had the lowest wake losses, with a value of 11.7 %, followed by the 6-line at 12.9% and the 3-line at 13.8%. Dynamic simulations were run in FAST.Farm to analyse the effects of wakes on mean anchor loads for wind headings that showed significant wake losses. The baseline farm showed the largest anchor load reductions due to wake effects (up to 16%), followed by the 3-line farm (up to 8%), and the 6-line farm, which showed relatively consistent load magnitudes on the 6-line anchors across all headings.

Modeling of Blockage and Wake Effect: Comparison with Field data

We apply engineering approaches for estimating the induction zone in front of a wind turbine and escalating to the global blockage at wind farm and cluster of farms scale as well accounting for wind farm wake. The methods include the vortex cylinder model and the self-similar model for blockage, and Jensen and Eddy-viscosity models for the wake estimation. To calibrate each model, Reynold Average Navier-Stokes with Actuator disc simulations are employed. Every model’s efficacy is assessed in comparison to the validation data of an operated wind farm in Scotland with more than 100 turbines of 10 MW each. We also examine the widely used process within the wind industry for transferring the application of calibrated wake and blockage models from one wind farm. According to our simulation analysis, blockage causes sideways acceleration and upstream slowdown. This also result in a gradient in the power produced by each turbine on the farm, which, if not properly accounted for, appears to be a wake model constraint. Furthermore, the response of different turbine type in another environment is not effectively predicted by the blockage and wake model, which is based on a particular turbine type and environment.

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.

Verification and Validation of Wind Farm Flow Models

Wind farm flow models allow for the analysis of wind power plant wake effects. There are several mathematical models available to describe this complex flow, and some of them are implemented across multiple code bases. This manuscript presents a framework for verification and validation of these models of different complexities. As part of this work, an API was developed to interface the different flow tools using the WindIO wind plant schema. Then, verification techniques are applied to the embedded tools as a test to make sure there are no mistakes in the codes, and across different flow models. This reveals similarities and differences in stochastic trends given realistic measurement uncertainty. Validation is performed using a database of large eddy simulations to quantify model agreement with high fidelity data and develop a predictor of what this implies for future simulations.

A simple RANS closure for wind-farms under neutral atmospheric conditions: Preliminary findings

Accurately predicting wind turbine wake effects is essential for optimizing wind-farm performance and minimizing maintenance costs. This study explores the applicability of the Sparse Regression of Turbulent Stress Anisotropy (SpaRTA) framework to develop a simple yet robust Reynolds-averaged Navier-Stokes (RANS) model for wake prediction in wind energy contexts. The framework introduces two correction terms into two-equation models, with k − ε model being utilized in the current study. One correction term resembles the residual of the Turbulent Kinetic Energy (TKE) equation, and the other corrects the deviatoric part of the Reynolds Stress Tensor (RST). The terms are calculated from high-fidelity measurement or simulation data, and symbolic regression is used to determine the model for these terms.In this study, Large Eddy Simulation (LES) data from a single turbine is used as the training dataset, and a sample pre-selection process is employed to discover a correction model efficiently. The derived model incorporates two terms based on Pope’s basis tensors and their invariants. The expression of the obtained model shows that it functions as a modification to the constant Cµ in the k − ε model. The model is evaluated by comparing its predicted velocity and TKE fields with the LES data used for the training. The model showed satisfactory performance in predicting both fields. Additionally, its generalizability is evaluated by testing it against a more complex six-turbine unseen case. The results indicate that the model effectively captures the velocity field and power output, but it tends to overpredict TKE, especially in the wake region.

Validation of induction/steering reserve-boosting active power control by a wind tunnel experiment with dynamic wind direction changes

This paper presents an experimental validation of an active power control methodology that combines induction control and wake steering to maximize the power reserve of the wind turbines in a farm. The experiments are carried out on a cluster of three scaled wind turbines in a boundary-layer wind tunnel, by dynamically varying the wind direction, and by considering a time-varying power tracking signal that mimics secondary frequency control of the grid. The methodology is compared with three active power control algorithms sourced from the literature. Results indicate that the instantaneous power reserves are indeed improved by an appropriate handling of wake effects, achieving a more accurate tracking of the power signal.

Comparison of helix and wake steering control for varying turbine spacing and wind direction

A variety of wind farm control strategies exist in order to reduce unfavorable wake effects in large wind farms. While strategies like wake steering already reached a high maturity level, it is interesting to compare them to more recently proposed strategies. Such a comparison can form the basis for the development of a symbiotic wind farm control toolbox, from which a control strategy is chosen and activated depending on the operating conditions. The present study compares wake steering with helix control across a wide range of turbine spacings and wind directions using large-eddy simulation (LES). The size of the search space is made computationally tractable for LES by adopting a setup based on one physical upstream turbine and a distribution of virtual downstream turbines which do not exert any thrust force. It is found that helix control is beneficial for full wake overlap and turbine spacing of less than six rotor diameters whereas wake steering proves to be optimal further downstream and for partial wake overlap. Furthermore, the results show that the helix control setpoint in the proximity of full wake overlap scenarios is less susceptible to wind direction variations. This finding indicates that the combination of wake steering and helix control has potential for the design of a wind farm controller which is more robust in full wake overlap scenarios and can reduce the need for large yaw offset adjustments.

Development of a numerical model for floating Vertical Axis Wind Turbines

Vertical Axis Wind Turbines (VAWTs), which are primarily used in small-scale applications, such as in remote or urban areas, could be particularly promising for floating offshore wind projects, where they offer benefits like increased stability, lower maintenance costs, and the potential for closer spacing due to lesser aerodynamic wake effects. However, to be competitive with floating HAWTs, scaling up VAWTs size is an urgent necessity to facilitate large-scale industrialization. To achieve this goal, the development of tools capable of assessing VAWT performance in challenging offshore environments is crucial. This work aims to introduce a time domain model of a floating VAWT, developed within the Matlab-Simscape environment. The model comprises a floating platform, a wind turbine, and a mooring system. The aerodynamics is simulated using the Double Multiple Stream Tube method, which relies on Blade Element Momentum (BEM) theory. Hydrodynamics is modelled using WEC-Sim, a Simscape library developed by NREL and SANDIA. Among the main advantages of the model are the flexibility and low computational time. The article presents a case study involving a semi-sub foundation, the OC4-DeepCwind, supporting a Darrieus H-rotor VAWT. The results obtained are compared with those from QBlade Ocean, an open-source tool developed by TU Berlin, demonstrating a good agreement between the two codes.

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

Current wind resources within the United States (US) indicate a potential to profitably install nearly 1,400 gigawatts of distributed wind (DW) capacity. This amount is equivalent to over half of the United States’ current energy demand from electricity, making it enough to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite the potential growth of DW in the US, deployments are presently hindered by a lack of confidence in resource estimation methods. One potential challenge is that smaller-scale turbines, with hub heights of 40 meters or less, are disproportionately impacted by obstacles such as buildings and vegetation. These obstacles may produce complex wake effects, best modeled with high-fidelity complex fluid dynamics (CFD) models that are too computationally expensive to use for routine siting and resource assessment. Thus, installers today make use of heuristics and simple equations to approximate the impact of obstacles while also leveraging long-term resource data from commercial or publicly available atmospheric models. This study evaluates these historical and commonly used methods alongside new lower-order obstacle models produced from CFD simulations and measurement-based bias correction. The preliminary results from this study show the importance of taking care in the choice and application of mesoscale atmospheric models and the significant value of bias correction using measurements from nearby meteorological towers. Detailed obstacle modeling provides only modest additional gains in performance and, in some cases, can add error, especially at sites where turbines have already been located to avoid obvious impact from upwind obstacles. These findings reinforce the importance of collecting in situ measurements and suggest that obstacle models may be better applied in practice to automated or computer-aided siting, rather than in economic wind resource assessments.

Mimicking the Helix with a porous disc for wind tunnel testing

A promising method to reduce wake effects in offshore wind farms is the Helix approach, which increases the mixing of the wake with the surrounding flow by exciting the individual blade pitch. This increases the wind speed in the wake, resulting in a higher power output at a downstream turbine. Wind tunnel testing is crucial to gather further understanding of the governing mechanisms behind the Helix and its efficiency in larger wind farm arrays. However, model turbines are expensive and complex. Porous Discs (PD) have proven to supply a less expensive and less complex alternative for wake-focused wind tunnel studies. In this study we present a novel PD model to mimic the Helix. The fundamental idea is to mimic the non-uniform, unsteady energy extraction over the rotor plane as observed at a Helix-controlled turbine. For this purpose, we derive a non-uniform porosity distribution over the PD, based on Large Eddy Simulations of a three-bladed turbine controlled with the Helix approach, and the actuator disc theory. The resulting non-uniform PD rotates at the excitation frequency described by the Strouhal number to mimic the Helix. We verified the novel experimental setup with smoke visualisation techniques and thrust measurements at a second PD in the wake and observed the typical characteristics of the Helix wake of a model turbine: First, the wake was deformed into a helical shape, and second, the wake velocity increased depending on the excitation Strouhal number.

Dynamic individual pitch control for wake mitigation: Why does the helix handedness in the wake matter?

Wind farm flow control aims at mitigating wake effects in order to maximize power production in wind farms. This work mostly focuses on the Helix strategy, which relies on individual pitch control to radially offset the application point of the thrust force from the rotor center and to dynamically change its azimuthal position. Previous studies have shown that power gains for a downstream turbine are higher for a counter-clockwise (CCW) rotation of the application point than for a clockwise (CW) one. In the CCW case, the wake develops as a right-handed helix, while in the CW case, a left-handed helix is observed. Using Large Eddy Simulations, this paper shows that the helix handedness in the wake matters due to its interaction with the wake swirl. Results of the CCW and CW helix first highlight the formation of streamwise vorticity in the near wake, which is transformed into strong coherent vortices in the far wake. Those vortex structures, to some extent similar to the counter-rotating vortex pair in the wake of yawed wind turbines, are responsible for (i) displacing the wake thanks to their induced velocities and (ii) deforming the shape of the wake.

Towards fine tuning wake steering policies in the field: an imitation-based approach

Yaw misalignment strategies can increase the power output of wind farms by mitigating wake effects, but finding optimal yaws requires overcoming both modeling errors and the growing complexity of the problem as the size of the farm grows. Recent works have therefore proposed decentralized multi-agent reinforcement learning (MARL) as a model-free, data-based alternative to learn online. These solutions have led to significant increases in total power production on experiments with both static and dynamic wind farms simulators. Yet experiments in dynamic simulations suggest that convergence time remains too long for online learning on real wind farms. As an improvement, baseline policies obtained by optimizing offline through steady-state models can be fed as inputs to an online reinforcement learning algorithm. This method however does not guarantee a smooth transfer of the policies to the real wind farm. This is aggravated when using function approximation approaches such as multi-layer neural networks to estimate policies and value functions. We propose an imitation approach, where learning a policy is first considered a supervised learning problem by deriving references from steady-state wind farm models, and then as an online reinforcement learning task for adaptation in the field. This approach leads to significant increases in the amount of energy produced over a lookup table (LUT) baseline on experiments done with the mid-fidelity dynamic simulator FAST.Farm under both static and varying wind conditions.

Power production and large-scale wake effects of offshore wind turbines with low specific rating

With the aim to increase the energy yield of large offshore wind farm clusters, especially during low wind speeds, innovative rotor concepts with low specific ratings are being developed. We use mesoscale simulations, to compare wakes and power production of a wind farm cluster with a rated power of 1.17 GW, equipped with a turbine featuring a low specific rating turbine concept, and the IEA 15 MW offshore reference turbine. While the IEA turbine resembles typical offshore wind turbine concepts, the HL rotor features a very large rotor diameter compared to its rated power, thus resulting in a very low specific rating for an offshore turbine. We simulate two wind farms made up of either of the two turbines that have the same installed capacity and farm layout. We found an improved power production of 86 % to 100 % for the HL wind farm in partial load conditions and comparable power production at rated operation. While the HL wind farm exhibits a more pronounced cluster wake during partial load operation, the IEA wind farm induces a larger wind speed deficit in the wake during rated operation. We discuss observed differences in wake behaviour, attributing them to the larger rotor diameter and lower thrust coefficient of the HL concept.

Multi-model approach for wind resource assessment

This study presents a multi-model approach for wind resource assessment of a wind farm affected by external wakes. The Weather Research and Forecasting model (WRF), a mesoscale model, is employed to simulate external wind farm wakes, while the Farm Optimization and eXtended yield Evaluation Software (FOXES), an engineering model, is used to simulate the wind farm of interest. This hybrid approach addresses the limitation of both models, mainly the lack of layout effects in mesoscale models and the poor representation of cluster wakes in engineering models. A case study, focusing on the Kaskasi wind farm in the Heligoland cluster, shows that the WRF model predicts larger wake losses compared to FOXES, with the multi-model approach yielding intermediate results. Systematic differences are found as a function of wind speed and seasonality, while the models behave differently as a function of turbulence intensity. The external wake effect was clearly identified for one wind direction sector in WRF and the multi-model approach, while FOXES failed to represent this. The proposed methodology does not only enhance classic resource assessment, but also facilitates efficient layout optimization using cluster waked inflow and allows for wind farm control studies, contributing to both planning and operational phases of wind farm management.

LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment

This work presents the validation of an extended version of the control-oriented, dynamic wind farm flow solver SPLINTER. The two-dimensional model is applied to use cases of wake steering by yaw misalignment and inflow wind direction variations and the results are compared to large-eddy simulations (LES). While SPLINTER is able to reproduce the antagonal behaviour of decreasing upstream and increasing downstream turbine power under wake deflection, a systematic deviation of the downstream power is detected and quantified, which is connected to underrepresented three-dimensional wake effects. In case of changing inflow wind direction, SPLINTER is capable of computing movement and shape of the bending wakes. The model smooths small-scale turbulent structures and disturbances and does not reproduce wake meandering, but manages to describe the evolution of the mean flow, which is tested by averaging over an ensemble of LES and comparing the resulting flow fields and turbine power time series. Under dynamic inflow conditions, SPLINTER is able to predict at which time intervals and at which rates downstream turbines will be influenced by wakes, which can improve the accuracy of short-term power and load forecasting and enables its application to online model predictive wind farm control.