Wake Wind Speed Research Articles

A Wind Tunnel Test for the Effect of Seed Tree Arrangement on Wake Wind Speed

Changes in canopy structures caused by harvesting and regeneration practices can significantly alter the wind environment. Therefore, it is essential to understand the wind patterns influenced by seed tree arrangements for predicting seed dispersal by winds and ensuring the success of natural regeneration. This study aimed to identify how wind speed responds to seed tree arrangement designs with differing horizontal distances, vertical positions, and free-stream wind speeds. A wind tunnel test was conducted using pine saplings for a scale model of various seed tree arrangements, and the change in wake speed was tracked. The wake’s relative wind speed averaged 71%, ranging from 3.5% to 108.5%, depending on the seed tree arrangement, distance from saplings, and vertical position. It peaked within the patch of three seed trees compared to other arrangements and at the top canopy layer. The empirical function effectively described the wind speed reduction and recovery with distance from saplings. For instance, the minimum wind speed was reached at 0.6–2.2 times the canopy height, and a wind speed reduction of over 20% of the free-stream wind speed was maintained at a 1.6–7.6 canopy height. A negative relationship between the seed tree leaf area and the relative wind speed was observed only at the top canopy layer. This study presents empirical evidence on the patterns of wake winds induced by different types of heterogeneous canopy structures.

A Nonlinear Wind Turbine Wake Expansion Model Considering Atmospheric Stability and Ground Effects

This study investigates the influence of atmospheric stability and ground effects on wind turbine wake recovery, challenging the conventional linear relationship between turbulence intensity and wake expansion coefficient. Through comprehensive field measurements and numerical simulations, we demonstrate that the linear wake expansion assumption is invalid at far-wake locations under high turbulence conditions, primarily due to ground effects. We propose a novel nonlinear wake expansion model that incorporates both atmospheric stability and ground effects by introducing a logarithmic relationship between the wake expansion coefficient and turbulence intensity. Validation results reveal the superior prediction accuracy of the proposed model compared to typical engineering wake models, with root mean square errors of wake wind speed predictions ranging from 0.04 to 0.063. The proposed model offers significant potential for optimizing wind farm layouts and enhancing overall wind energy production efficiency.

Land-based wind plant wake characterization using dual-Doppler radar measurements at AWAKEN

Wind plant wakes have been shown to persist for tens of kilometers downstream in offshore environments, reducing the power output of neighboring plants, but their behavior on land remains relatively unexplored through observation. This study capitalizes on the unique and extensive field data collected for the American WAKE ExperimeNt (AWAKEN) project underway in northern Oklahoma. X-band dual-Doppler radars deployed at this site measure wind speed and direction at 25-m and 2-min resolution within a 30-km range, capturing the interactions between three neighboring wind plants. These measurements show that the wake of one wind plant extends at least 15 km downstream under easterly wind and stable atmospheric conditions. Though the wake wind speed increases within the first 10 km, it plateaus at 90% of the freestream wind speed. The spanwise velocity distribution within the wake initially shows the clear signature of the wind plant layout, which is smoothed as it propagates downstream, indicating spanwise momentum transfer is a key mechanism in wind plant wake development and recovery. These findings have important implications for wind plant siting decisions and resource assessments, and provide insights into atmospheric interactions at the wind plant scale.

Optimal combined wake and active power control of large‐scale wind farm considering available power

AbstractThe high‐order nonlinear complex characteristics of traditional static wake model (SWM) restrict the fast optimization of wake effect. Therefore, this paper introduces a dynamic wake model (DWM) to describe the time‐varying wake characteristics of wind turbines (WTs) with low computational cost. The traditional SWM is linearized to derive the wake wind speed sensitivity coefficients, which represents the sensitivity of wake wind speed deficit with respect to the active power reference. Considering the natural propagation characteristics of wake effect, a wake delay function is added to realize the future wind speed prediction of different locations of the wind farm. And a joint control strategy of wake and active power based on model predictive control (MPC) is proposed to optimize the power contribution of each WT to minimize wake effects, and maximize total available power. Thus, the increased available power is able to satisfy the power demand from Transmission System Operator (TSO) for ancillary services and enhance power support capability. The control performance of the proposed control strategy is evaluated by simulations under constant and varying incoming wind speed.

Verification of Prediction Method Based on Machine Learning under Wake Effect Using Real-Time Digital Simulator

With the increase in the penetration rate of renewable energy sources, a machine-learning-based forecasting system has been introduced to the grid sector to improve the participation rate in the electricity market and reduce energy losses. In these studies, correlation analysis of mechanical and environmental variables, including geographical figures, is considered a crucial point to increase the prediction’s accuracy. Various models have been applied in terms of accuracy, speed calculation, and amount of data based on a mathematical model that can calculate the wake; however, it can be difficult to derive variables such as air density, roughness length, and the effect of turbulence on the structural characteristics of wind turbines. Furthermore, wake accuracy could decrease due to the excessive variables that come from the wake effect parameters. In this paper, we intend to conduct research to improve prediction accuracy by considering the wake effect of wind turbines using supervisory control and data acquisition (SCADA) data from the Dongbok wind farm. The wake divides the wind direction into four parts and then recognizes and predicts the affected wind turbine. The predicted result is the wake wind speed and its conversion to power generation by applying a power curve. We try to show the efficiency of machine learning by comparing the wake wind speed and the power generation in the wake model. This result shows the error rate using evaluation metrics of regression, such as mean squared error (MSE), root mean squared error (RMSE), and weighted absolute percentage error (WAPE), and attempts to verify power system impact and efficiency through a real-time digital simulator (RTDS).

A study on measuring wind turbine wake based on UAV anemometry system

Unmanned aerial vehicle (UAV) wind measurement is a newly developed wind measurement method that can improve the efficiency of conventional wind measurements. A method for measuring the wake characteristics of wind turbines based on UAV is proposed. The research results show that when the downstream distance was 1D, the wake wind speed and turbulence intensity at the hub height are about 60–70% and 130–150% of the incoming flow, respectively. When the downstream distance was 10D, the wake wind speed and turbulence intensity at the hub height gradually recovered to about 90% and 110% of the incoming flow, respectively, and the wake width in the area was about 2.5 times the diameter of the turbine. The wind speed measured by UAV was in good agreement with that predicted by the AV model and the 3DJG model, indicating that the Gaussian-shape curve could better describe the wake wind speed distribution. The UAV anemometry system provides a new research approach for wind turbine wake wind field measurement and the assessment of in-service wind farm resources. Moreover, the results from this study provide a reference for the determination of wind turbine wake, generation power prediction, and structure design.

Wind Farm Energy Production Optimization Via Wake Steering

The wake steering technique consists in misaligning turbines with respect to the incoming wind with the goal of displacing their wake region and reducing the wake wind speed deficit, thus increasing the power production of other turbines downwind. In this paper, an algorithm is proposed to estimate the amount of production gain a wind farm could achieve by employing this technique. Details about data treatment are provided and an analytical wake model that can represent the wakes displacement is described. A minimization algorithm to calibrate the wake model parameters based on SCADA data is presented, and the complete math needed to solve this problem is developed. Similarly, to find the optimal yaw angle values that maximize the wind farm production, a maximization problem is described along with the full development of the equations behind it. Insights on an efficient computational implementation to solve these problems are shown, based on matrix representation of the described variables. A case study is presented for a small wind farm owned by Voltalia, consisting of five turbines. Results point to an AEP gain in the order of 1%, a relevant value over the course of a project’s lifetime.

Aerodynamic Rotor Performance of a 3300-kW Modern Commercial Large-Scale Wind Turbine Installed in a Wind Farm

Abstract Wind turbine technology in the world has been developed by continuously improving turbine performance, design, and efficiency. Over the last 40 years, the rated capacity and dimension of the commercial wind turbines have increased dramatically, so the energy cost has declined significantly, and the industry has moved from an idealistic position to an acknowledged component of the power generation industry. For this reason, a thorough examination of the aerodynamic rotor performance of a modern large-scale wind turbine working on existing onshore wind farms is critically important to monitor and control the turbine performance and also for forecasting turbine power. This study focuses on the aerodynamic rotor performance of a 3300-kW modern commercial large-scale wind turbine operating on an existing onshore wind farm based on the measurement data. First, frequency distributions of wind speeds and directions were obtained using measurements over one year. Then, wind turbine parameters such as free-stream wind speed (U∞), far wake wind speed (UW), axial flow induction factor (a), wind turbine power coefficient (CP), tangential flow induction factor (a′), thrust force coefficient (CT), thrust force (T), tip-speed ratio (λ), and flow angle (ϕ) were calculated using the measured rotor disc wind speed (UD), atmospheric air temperature (Tatm), turbine rotational speed (Ω), and turbine power output (P) parameters. According to the results obtained, the maximum P, CP, CT, T, and Ω were calculated as approximately 3.3 MW, 0.45, 0.6, 330 kN, and 12.9 rpm, respectively, while the optimum λ, ϕ, U∞, and Ω for the maximum CP were determined as 7.5–8.5, 6–6.3°, 5–10 m/s, and 6–10 rpm, respectively. These calculated results can contribute to assessing the economic and technical feasibility of modern commercial large-scale wind turbines and supporting future developments in wind energy and turbine technology.

Wind Turbine Wake Modeling in Accelerating Wind Field: A Preliminary Study on a Two-Dimensional Hill

Complex terrain can influence wind turbine wakes and wind speed profiles in a wind farm. Consequently, predicting the performance of wind turbines and energy production over complex terrain is more difficult than it is over flat terrain. In this preliminary study, an engineering wake model, that considers acceleration on a two-dimensional hill, was developed based on the momentum theory. The model consists of the wake width and wake wind speed. The equation to calculate the rotor thrust, which is calculated by the wake wind speed profiles, was also formulated. Then, a wind-tunnel test was performed in simple flow conditions in order to investigate wake development over a two-dimensional hill. After this the wake model was compared with the wind-tunnel test, and the results obtained by using the new wake model were close to the wind-tunnel test results. Using the new wake model, it was possible to estimate the wake shrinkage in an accelerating two-dimensional wind field.

Implementation of a generalized actuator line model for wind turbine parameterization in the Weather Research and Forecasting model

A generalized actuator line (GAL) wind turbine parameterization is implemented within the Weather Research and Forecasting model to enable high-fidelity large-eddy simulations of wind turbine interactions with boundary layer flows under realistic atmospheric forcing conditions. Numerical simulations using the GAL parameterization are evaluated against both an already implemented generalized actuator disk (GAD) wind turbine parameterization and two field campaigns that measured the inflow and near-wake regions of a single turbine. The representation of wake wind speed, variance, and vorticity distributions is examined by comparing fine-resolution GAL and GAD simulations and GAD simulations at both fine and coarse-resolutions. The higher-resolution simulations show slightly larger and more persistent velocity deficits in the wake and substantially increased variance and vorticity when compared to the coarse-resolution GAD. The GAL generates distinct tip and root vortices that maintain coherence as helical tubes for approximately one rotor diameter downstream. Coarse-resolution simulations using the GAD produce similar aggregated wake characteristics to both fine-scale GAD and GAL simulations at a fraction of the computational cost. The GAL parameterization provides the capability to resolve near wake physics, including vorticity shedding and wake expansion.

Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics

Abstract. Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors. To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s−1 at the turbine hub-height of 80 m. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30% of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: cross-stream component errors are on the order of 15% of the hub-height inflow wind speed (1.0 m s−1) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 m s−1 (or 15% of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8% while vertical velocity estimates are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.

Wind wake influence estimation on energy production of wind farm by adaptive neuro-fuzzy methodology

Dissection of the power output in a row of working turbines and the reliance on wind course in respect to the column heading is examined. The point is to portray the extent of the wake impacts and give a sign of the indication of wind bearing. The target is to delineate whether the uniting of wakes inside extensive wind homesteads can be depicted by basic direct models or whether the consideration of the two-route collaboration between the wind turbines and the limit layer is a fundamental essential for precise models of wakes to be utilized within future wind farm plan. Soft computing methodologies may be utilized as substitute for analytical approach since they provides some benefits such as no need to information of internal system parameters, compact solution for multi-variable problems. This investigation dealt with application of ANFIS (adaptive neuro-fuzzy inference system) for predicting the wake power and wind speed deficit. To provide statistical analysis, RMSE (root mean square error), coefficient of determination (R2) and Pearson coefficient (r) were utilized. The study results suggested that ANFIS would be an efficient soft computing methodology to offer precise predictions of wake wind speed deficit and power deficit ratio in wind farms. According to the achieved results, the best prediction was observed for free wind speed of 8 m/s. The RMSE, R2 and r were computed as 0.0763, 0.9893 and 0.9946 for ANFIS prediction of wake wind speed deficit and as 0.0128, 0.9967 and 0.9984 for ANFIS prediction of power deficit ratio.

RETRACTED ARTICLE: Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect

We present a project management methodology designed for the selection of wind turbines wake effect most influential parameters, who need to run wind farm project for large energy conversion. Very frequently, the managers of these projects are not project management professionals, so they need guidance to have autonomy, using minimal time and documentation resources. Therefore, agile method is adapted to assist the project management. Wind energy poses challenges such as the reduction in the wind speed due to the wake effect by other turbines. If a turbine is within the area of turbulence caused by another turbine, or the area behind another turbine, the wind speed suffers a reduction and, therefore, there is a decrease in the production of electricity. In order to increase the efficiency of a wind farm, analyzing the parameters, which have influence on the wake effect, is one of the focal research areas. To maximize the power produced in a wind farm, it is important to determine and analyze the most influential factors on the wake effects or wake wind speeds since the effect has most influence on the produced power. This procedure is typically called variable selection, and it corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. In this study, architecture for modeling complex systems in function approximation and regression was used, based on using adaptive neuro-fuzzy inference system (ANFIS). Variable searching using the ANFIS network was performed to determine how the five parameters affect the wake wind speed. Our article answers the call for renewing the theoretical bases of wind farm project management in order to overcome the problems that stem from the application of methods based on decision-rationality norms, which bracket the complexity of action and interactions in projects.

Experimental and numerical studies on the wake behavior of a horizontal axis wind turbine

The wake characteristics of a horizontal axis wind turbine have been investigated, both experimentally and numerically. The computational numerical solution was carried out for the full rotor model, using a Navier–Stokes solver, employing the k–ε model appropriately modified for the atmospheric flows. The experiments were conducted at a 2MW wind turbine for the measurements of upstream wind speed profiles, using SOund Detection and Ranging (SODAR), and wake wind speed profiles using Light Detection and Ranging (LIDAR), at varying distances between 2 and 7 times the rotor diameters downstream of the wind turbine. The evaluation of the computational model is made by comparing the predicted and measured velocities at the prescribed downstream locations at different upstream wind speeds. The present numerical setup employing the k–ε model and the fully mesh resolved rotor shows good agreement with the measurements. The model is used to find the relation between the wind speed and the wake recovery.

Comparison of Wake Model Simulations with Offshore Wind Turbine Wake Profiles Measured by Sodar

Abstract This paper gives an evaluation of most of the commonly used models for predicting wind speed decrease (wake) downstream of a wind turbine. The evaluation is based on six experiments where free-stream and wake wind speed profiles were measured using a ship-mounted sodar at a small offshore wind farm. The experiments were conducted at varying distances between 1.7 and 7.4 rotor diameters downstream of the wind turbine. Evaluation of the models compares the predicted and observed velocity deficits at hub height. A new method of evaluation based on determining the cumulative momentum deficit over the profiles is described. Despite the apparent simplicity of the experiments, the models give a wide range of predictions. Overall, it is not possible to establish any of the models as having individually superior performance with respect to the measurements.