Articles | Volume 5, issue 4
Wind Energ. Sci., 5, 1425–1434, 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue: Wind Energy Science Conference 2019
Research article 30 Oct 2020
Research article | 30 Oct 2020
Theory and verification of a new 3D RANS wake model
Philip Bradstock and Wolfgang Schlez
Related subject area
Wind and turbulenceNew methods to improve the vertical extrapolation of near-surface offshore wind speedsWind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievalsA pressure-driven atmospheric boundary layer model satisfying Rossby and Reynolds number similarityDesign and analysis of a wake model for spatially heterogeneous flowEvaluation of tilt control for wind-turbine arrays in the atmospheric boundary layerEvaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250 m meteorological mastWind turbines in atmospheric flow: fluid–structure interaction simulations with hybrid turbulence modelingOffshore wind farm global blockage measured with scanning lidarUnderstanding and mitigating the impact of data gaps on offshore wind resource estimatesInvestigating the loads and performance of a model horizontal axis wind turbine under reproducible IEC extreme operational conditionsValidation of the dynamic wake meandering model with respect to loads and power productionMethod for airborne measurement of the spatial wind speed distribution above complex terrainAxial induction controller field test at Sedini wind farmWake redirection at higher axial inductionAn overview of wind-energy-production prediction bias, losses, and uncertaintiesUtilizing physics-based input features within a machine learning model to predict wind speed forecasting errorSet-point optimization in wind farms to mitigate effects of flow blockage induced by atmospheric gravity wavesCalibration and validation of the Dynamic Wake Meandering model Part I: Bayesian estimation of model parameters using SpinnerLidar-derived wake characteristicsField experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in ItalyComputational analysis of high-lift-generating airfoils for diffuser-augmented wind turbinesAeroelastic analysis of wind turbines under turbulent inflow conditionsParameterization of wind evolution using lidarMountain waves can impact wind power generationObservations and simulations of a wind farm modifying a thunderstorm outflow boundaryThe Alaiz experiment: untangling multi-scale stratified flows over complex terrainExperimental and numerical simulation of extreme operational conditions for horizontal axis wind turbines based on the IEC standardGlobal trends in the performance of large wind farms based on high-fidelity simulationsThe most similar predictor – on selecting measurement locations for wind resource assessmentMitigation of offshore wind power intermittency by interconnection of production sitesChanging the rotational direction of a wind turbine under veering inflow: a parameter studyOptimal tuning of engineering wake models through lidar measurementsWRF-Simulated Low-Level Jets over Iowa: Characterization and Sensitivity StudiesEvaluation of the lattice Boltzmann method for wind modelling in complex terrainThe digital terrain model in the computational modelling of the flow over the Perdigão site: the appropriate grid sizeMinute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurementsDoes the rotational direction of a wind turbine impact the wake in a stably stratified atmospheric boundary layer?Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical modelsCorrelations of power output fluctuations in an offshore wind farm using high-resolution SCADA dataAn alternative form of the super-Gaussian wind turbine wake modelMultipoint reconstruction of wind speedsExtreme Wind Shear Events in US Offshore Wind Energy Areas and the Role of Induced StratificationHow wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbineAeroelastic load validation in wake conditions using nacelle-mounted lidar measurementsMulti-lidar wind resource mapping in complex terrainClustering wind profile shapes to estimate airborne wind energy productionValidation of Sentinel-1 offshore winds and average wind power estimation around IrelandValidation of uncertainty reduction by using multiple transfer locations for WRF–CFD coupling in numerical wind energy assessmentsDecreasing wind speed extrapolation error via domain-specific feature extraction and selectionDynamic wake meandering model calibration using nacelle-mounted lidar systemsFirst characterization of a new perturbation system for gust generation: the chopper
Mike Optis, Nicola Bodini, Mithu Debnath, and Paula Doubrawa
Wind Energ. Sci., 6, 935–948,Short summary
Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 m. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine-learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 6, 841–866,Short summary
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
Maarten Paul van der Laan, Mark Kelly, and Mads Baungaard
Wind Energ. Sci., 6, 777–790,Short summary
Wind farms operate in the atmospheric boundary layer, and their performance is strongly dependent on the atmospheric conditions. We propose a simple model of the atmospheric boundary layer that can be used as an inflow model for wind farm simulations for isolating a number of atmospheric effects – namely, the change in wind direction with height and atmospheric boundary layer depth. In addition, the simple model is shown to be consistent with two similarity theories.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758,Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Wind Energ. Sci., 6, 663–675,Short summary
We deal with wake redirection, which is a promising approach designed to mitigate turbine–wake interactions which have a negative impact on the performance and lifetime of wind farms. We show that substantial power gains can be obtained by tilting the rotors of spanwise-periodic wind-turbine arrays in the atmospheric boundary layer (ABL). Optimal relative rotor sizes and spanwise spacings exist, which maximize the global power extracted from the wind.
Alfredo Peña, Branko Kosović, and Jeffrey D. Mirocha
Wind Energ. Sci., 6, 645–661,Short summary
We investigate the ability of a community-open weather model to simulate the turbulent atmosphere by comparison with measurements from a 250 m mast at a flat site in Denmark. We found that within three main atmospheric stability regimes, idealized simulations reproduce closely the characteristics of the observations with regards to the mean wind, direction, turbulent fluxes, and turbulence spectra. Our work provides foundation for the use of the weather model in multiscale real-time simulations.
Christian Grinderslev, Niels Nørmark Sørensen, Sergio González Horcas, Niels Troldborg, and Frederik Zahle
Wind Energ. Sci., 6, 627–643,Short summary
This study investigates aero-elasticity of wind turbines present in the turbulent and chaotic wind flow of the lower atmosphere, using fluid–structure interaction simulations. This method combines structural response computations with high-fidelity modeling of the turbulent wind flow, using a novel turbulence model which combines the capabilities of large-eddy simulations for atmospheric flows with improved delayed detached eddy simulations for the separated flow near the rotor.
Jörge Schneemann, Frauke Theuer, Andreas Rott, Martin Dörenkämper, and Martin Kühn
Wind Energ. Sci., 6, 521–538,Short summary
A wind farm can reduce the wind speed in front of it just by its presence and thus also slightly impact the available power. In our study we investigate this so-called global-blockage effect, measuring the inflow of a large offshore wind farm with a laser-based remote sensing method up to several kilometres in front of the farm. Our results show global blockage under a certain atmospheric condition and operational state of the wind farm; during other conditions it is not visible in our data.
Julia Gottschall and Martin Dörenkämper
Wind Energ. Sci., 6, 505–520,
Kamran Shirzadeh, Horia Hangan, Curran Crawford, and Pooyan Hashemi Tari
Wind Energ. Sci., 6, 477–489,Short summary
Wind energy systems work coherently in atmospheric flows which are gusty. This causes highly variable power productions and high fatigue loads on the system, which together hold back further growth of the wind energy market. This study demonstrates an alternative experimental procedure to investigate some extreme wind condition effects on wind turbines based on the IEC standard. This experiment can be improved upon and used to develop new control concepts, mitigating the effect of gusts.
Inga Reinwardt, Levin Schilling, Dirk Steudel, Nikolay Dimitrov, Peter Dalhoff, and Michael Breuer
Wind Energ. Sci., 6, 441–460,Short summary
This analysis validates the DWM model based on loads and power production measured at an onshore wind farm. Special focus is given to the performance of a version of the DWM model that was previously recalibrated with a lidar system at the site. The results of the recalibrated wake model agree very well with the measurements. Furthermore, lidar measurements of the wind speed deficit and the wake meandering are incorporated in the DWM model definition in order to decrease the uncertainties.
Christian Ingenhorst, Georg Jacobs, Laura Stößel, Ralf Schelenz, and Björn Juretzki
Wind Energ. Sci., 6, 427–440,Short summary
Wind farm sites in complex terrain are subject to local wind phenomena, which are difficult to quantify but have a huge impact on a wind turbine's annual energy production. Therefore, a wind sensor was applied on an unmanned aerial vehicle and validated against stationary wind sensors with good agreement. A measurement over complex terrain showed local deviations from the mean wind speed of approx. ± 30 %, indicating the importance of an extensive site evaluation to reduce investment risk.
Ervin Bossanyi and Renzo Ruisi
Wind Energ. Sci., 6, 389–408,Short summary
This paper describes the design and field testing of a controller for reducing wake interactions on a wind farm. Reducing the power of some turbines weakens their wakes, allowing other turbines to produce more power so that the total wind farm power may increase. There have been doubts that this is feasible, but these field tests on a full-scale wind farm indicate that this goal has been achieved, also providing convincing validation of the model used for designing the controller.
Wind Energ. Sci., 6, 377–388,Short summary
In this study wake redirection and axial-induction control are combined to mitigate turbine–wake interactions, which have a negative impact on the performance and lifetime of wind farms. The results confirm that substantial power gains are obtained when overinduction is combined with tilt control. More importantly, the approach is extended to the case of yaw control, showing that large power gain enhancements are obtained by means of static overinductive yaw control.
Joseph C. Y. Lee and M. Jason Fields
Wind Energ. Sci., 6, 311–365,Short summary
This review paper evaluates the energy prediction bias in the wind resource assessment process, and the overprediction bias is decreasing over time. We examine the estimated and observed losses and uncertainties in energy production from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 standard. The considerable uncertainties call for further improvements in the prediction methodologies and more observations for validation.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 6, 295–309,Short summary
Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.
Luca Lanzilao and Johan Meyers
Wind Energ. Sci., 6, 247–271,Short summary
This research paper investigates the potential of thrust set-point optimization in large wind farms for mitigating gravity-wave-induced blockage effects for the first time, with the aim of increasing the wind-farm energy extraction. The optimization tool is applied to almost 2000 different atmospheric states. Overall, power gains above 4 % are observed for 77 % of the cases.
Davide Conti, Nikolay Dimitrov, Alfredo Peña, and Thomas Herges
Wind Energ. Sci. Discuss.,
Revised manuscript under review for WESShort summary
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high spatial and temporal resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected as part of the Small Wind Farm Technology (SWiFT) experiment in Texas. The analysis includes both the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric stability conditions.
Bart M. Doekemeijer, Stefan Kern, Sivateja Maturu, Stoyan Kanev, Bastian Salbert, Johannes Schreiber, Filippo Campagnolo, Carlo L. Bottasso, Simone Schuler, Friedrich Wilts, Thomas Neumann, Giancarlo Potenza, Fabio Calabretta, Federico Fioretti, and Jan-Willem van Wingerden
Wind Energ. Sci., 6, 159–176,Short summary
This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.
Aniruddha Deepak Paranjape, Anhad Singh Bajaj, Shaheen Thimmaiah Palanganda, Radha Parikh, Raahil Nayak, and Jayakrishnan Radhakrishnan
Wind Energ. Sci., 6, 149–157,Short summary
This project is a comparative study that takes into consideration various airfoils from the Selig, NACA, and Eppler families and models them as diffusers of the wind turbine. The efficiency of the diffuser-augmented wind turbine can be enhanced by optimizing the geometry of the diffuser shape. Their subsequent performance trends were then analyzed, and the lower-performing airfoils were systematically eliminated to leave us with an optimum design.
Giorgia Guma, Galih Bangga, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 6, 93–110,Short summary
With the increase in installed wind capacity, the rotor diameter of wind turbines is becoming larger and larger, and therefore it is necessary to take aeroelasticity into consideration. At the same time, wind turbines are in reality subjected to atmospheric inflow leading to high wind instabilities and fluctuations. Within this work, a high-fidelity chain is used to analyze the effects of both by the use of models of the same turbine with increasing complexity and technical details.
Yiyin Chen, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 6, 61–91,Short summary
Wind evolution is currently of high interest, mainly due to the development of lidar-assisted wind turbine control (LAC). Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into 3D simulations to provide a more realistic simulation environment for LAC. Motivated by these factors, we investigate the potential of Gaussian process regression in the parameterization of a two-parameter wind evolution model using data of two nacelle-mounted lidars.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60,Short summary
Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and Weather Research and Forecasting model simulations. This research describes how mountain waves form in the Columbia River basin and affect wind energy production and their impact on operational forecasting, wind plant layout, and integration of power into the grid.
Jessica M. Tomaszewski and Julie K. Lundquist
Wind Energ. Sci., 6, 1–13,Short summary
We use a mesoscale numerical weather prediction model to conduct a case study of a thunderstorm outflow passing over and interacting with a wind farm. These simulations and observations from a nearby radar and surface station confirm that interactions with the wind farm cause the outflow to reduce its speed by over 20 km h−1, with brief but significant impacts on the local meteorology, including temperature, moisture, and winds. Precipitation accumulation across the region was unaffected.
Pedro Santos, Jakob Mann, Nikola Vasiljević, Elena Cantero, Javier Sanz Rodrigo, Fernando Borbón, Daniel Martínez-Villagrasa, Belén Martí, and Joan Cuxart
Wind Energ. Sci., 5, 1793–1810,Short summary
This study presents results from the Alaiz experiment (ALEX17), featuring the characterization of two cases with flow features ranging from 0.1 to 10 km in complex terrain. We show that multiple scanning lidars can capture in detail a type of atmospheric wave that can happen up to 10 % of the time at this site. The results are in agreement with multiple ground observations and demonstrate the role of atmospheric stability in flow phenomena that need to be better captured by numerical models.
Kamran Shirzadeh, Horia Hangan, and Curran Crawford
Wind Energ. Sci., 5, 1755–1770,Short summary
The main goal of this study is to develop a physical simulation of some extreme wind conditions that are defined by the IEC standard. This has been performed by a hybrid numerical–experimental approach with a relevant scaling. Being able to simulate these dynamic flow fields can generate decisive results for future scholars working in the wind energy sector to make these wind energy systems more reliable and finally helps to accelerate the reduction of the cost of electricity.
Søren Juhl Andersen, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1689–1703,Short summary
The complexity of wind farm operation increases as the wind farms get larger and larger. Therefore, researchers from three universities have simulated numerous different large wind farms as part of an international benchmark. The study shows how simple engineering models can capture the general trends, but high-fidelity simulations are required in order to quantify the variability and uncertainty associated with power production of the wind farms and hence the potential profitability and risks.
Andreas Bechmann, Juan Pablo M. Leon, Bjarke T. Olsen, and Yavor V. Hristov
Wind Energ. Sci., 5, 1679–1688,Short summary
When assessing wind resources for wind farm development, the first step is to measure the wind from tall meteorological masts. As met masts are expensive, they are not built at every planned wind turbine position but sparsely while trying to minimize the distance. However, this paper shows that it is better to focus on the
similaritybetween the met mast and the wind turbines than the distance. Met masts at similar positions reduce the uncertainty of wind resource assessments significantly.
Ida Marie Solbrekke, Nils Gunnar Kvamstø, and Asgeir Sorteberg
Wind Energ. Sci., 5, 1663–1678,Short summary
The potential of collective offshore wind power is quantified using 16 years of hourly wind speed observations. Wind power intermittency is reduced through a hypothetical electricity grid connecting five sites at the Norwegian continental shelf. We identify large-scale atmospheric situations resulting in long-term periods of power shutdown. Wind power variability and risk measures decrease in an interconnected wind power system.
Antonia Englberger, Julie K. Lundquist, and Andreas Dörnbrack
Wind Energ. Sci., 5, 1623–1644,Short summary
Wind turbines rotate clockwise. The rotational direction of the rotor interacts with the nighttime veering wind, resulting in a rotational-direction impact on the wake. In the case of counterclockwise-rotating blades the streamwise velocity in the wake is larger in the Northern Hemisphere whereas it is smaller in the Southern Hemisphere.
Lu Zhan, Stefano Letizia, and Giacomo Valerio Iungo
Wind Energ. Sci., 5, 1601–1622,Short summary
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.
Jeanie A. Aird, Rebecca J. Barthelmie, Tristan J. Shepherd, and Sara C. Pryor
Wind Energ. Sci. Discuss.,
Revised manuscript accepted for WESShort summary
Low-level jets (LLJ) are pronounced maxima in wind speed profiles affecting wind turbine performance and longevity. We present a climatology of LLJ over Iowa using output from the Weather Research and Forecasting Model (WRF) and determine the rotor plane conditions when they occur. LLJ characteristics are highly sensitive to the identification criteria applied. LLJ characteristics also vary with different model output resolution but spatial distributions of areas of occurrence are preserved.
Alain Schubiger, Sarah Barber, and Henrik Nordborg
Wind Energ. Sci., 5, 1507–1519,Short summary
A large-eddy simulation using the lattice Boltzmann method (LBM) Palabos framework was implemented to calculate the wind field over the complex terrain of Bolund Hill. The results were compared to Reynolds-averaged Navier–Stokes and detached-eddy simulation (DES) using Ansys Fluent and field measurements. A comparison of the three methods' computational costs has shown that the LBM, even though not yet fully optimised, can perform 5 times faster than DES and lead to reasonably accurate results.
José M. L. M. Palma, Carlos A. M. Silva, Vítor C. Gomes, Alexandre Silva Lopes, Teresa Simões, Paula Costa, and Vasco T. P. Batista
Wind Energ. Sci., 5, 1469–1485,Short summary
The digital terrain model is the first input in the computational modelling of atmospheric flows. The ability of thee meshes (high-, medium- and low-resolution) to replicate the Perdigão experiment site was appraised in two ways: by their ability to replicate the terrain attributes, elevation and slope and by their effect on the wind flow computational results. At least 40 m horizontal resolution is required in computational modelling of the flow over Perdigão.
Frauke Theuer, Marijn Floris van Dooren, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 5, 1449–1468,Short summary
Very short-term wind power forecasts are gaining increasing importance with the rising share of renewables in today's energy system. In this work, we developed a methodology to forecast wind power of offshore wind turbines on minute scales utilising long-range single-Doppler lidar measurements. The model was able to outperform persistence during unstable stratification in terms of deterministic and probabilistic scores, while it showed large shortcomings for stable atmospheric conditions.
Antonia Englberger, Andreas Dörnbrack, and Julie K. Lundquist
Wind Energ. Sci., 5, 1359–1374,Short summary
At night, the wind direction often changes with height, and this veer affects structures near the surface like wind turbines. Wind turbines usually rotate clockwise, but this rotational direction interacts with veer to impact the flow field behind a wind turbine. If another turbine is located downwind, the direction of the upwind turbine's rotation will affect the downwind turbine.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Jason Roadman, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci., 5, 1253–1272,Short summary
A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
Janna K. Seifert, Martin Kraft, Martin Kühn, and Laura J. Lukassen
Wind Energ. Sci. Discuss.,
Revised manuscript accepted for WESShort summary
Fluctuations in the power output of wind turbines are one of the major challenges in the integration and utilisation of wind energy. By analysing the power output fluctuations of wind turbine pairs in an offshore wind farm, we show that their correlation is depending on the location within in the wind farm and their inflow. The main outcome is that these correlation dependencies can be characterised by statistics of the power output of the wind turbines and sorted by a clustering algorithm.
Frédéric Blondel and Marie Cathelain
Wind Energ. Sci., 5, 1225–1236,Short summary
Analytical wind turbine wake models are of high interest for wind farm designers: they provide an estimation of wake losses for a given layout at a low computational cost. Consequently they are heavily used for wind farm design and power production evaluation. While most analytical models focus on far-wake characteristics, we propose an approach that is able to represent both near- and far-wake velocity deficit, enabling the simulation of closely packed wind farms.
Christian Behnken, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci., 5, 1211–1223,Short summary
We extend the common characterisation and modelling of wind time series with respect to higher-order statistics. We present an approach which enables us to obtain the general multipoint statistics of wind time series measured. This work is an important step in a more comprehensive description of wind also including extreme events. Important is that we show how stochastic equations can be derived from measured wind data which can be used to model long time series.
Mithu Debnath, Paula Doubrawa, Mike Optis, Patrick Hawbecker, and Nicola Bodini
Wind Energ. Sci. Discuss.,
Revised manuscript accepted for WESShort summary
As the offshore wind industry emerges on the U.S. East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. We leverage a year of two floating lidars data to quantify and characterize the frequent occurrence of high wind shear and low-level jet events, both of which will have considerable impact on turbine operation. We find that almost 100 independent long events occur throughout the year.
Patrick Murphy, Julie K. Lundquist, and Paul Fleming
Wind Energ. Sci., 5, 1169–1190,Short summary
We present and evaluate an improved method for predicting wind turbine power production based on measurements of the wind speed and direction profile across the rotor disk for a wind turbine in complex terrain. By comparing predictions to actual power production from a utility-scale wind turbine, we show this method is more accurate than methods based on hub-height wind speed or surface-based atmospheric characterization.
Davide Conti, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 5, 1129–1154,Short summary
We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. The uncertainty of aeroelastic load predictions is quantified against wind turbine on-board sensor data. This work demonstrates the applicability of nacelle-mounted lidar measurements to extend load and power validations under wake conditions and highlights the main challenges.
Robert Menke, Nikola Vasiljević, Johannes Wagner, Steven P. Oncley, and Jakob Mann
Wind Energ. Sci., 5, 1059–1073,Short summary
The estimation of wind resources in complex terrain is challenging as the wind conditions change significantly over short distances, different to flat terrain, where spatial changes are small. We demonstrate in this work that wind lidars can remotely map wind resources over large areas. This will have implications for the planning of wind energy projects and ultimately reduce uncertainties in wind resource estimations in complex terrain, making such areas more interesting for future development.
Mark Schelbergen, Peter C. Kalverla, Roland Schmehl, and Simon J. Watson
Wind Energ. Sci., 5, 1097–1120,Short summary
We have presented a methodology for including multiple wind profile shapes in a wind resource description that are identified using a data-driven approach. These shapes go beyond the height range for which conventional wind profile relationships are developed. Moreover, they include non-monotonic shapes such as low-level jets. We demonstrated this methodology for an on- and offshore reference location using DOWA data and efficiently estimated the annual energy production of a pumping AWE system.
Louis de Montera, Tiny Remmers, Ross O'Connell, and Cian Desmond
Wind Energ. Sci., 5, 1023–1036,Short summary
This paper provides a validation of satellite-acquired wind speed measurements (Sentinel-1 synthetic aperture radar Level 2 OCN) against four weather buoys and three coastal stations located around Ireland. It is shown that such satellite measurements provide a accurate assessment of long-term wind resource characteristics for both coastal and far from shore locations. This work was conducted in order to allow the use of satellite data in the planning of offshore wind farms in Ireland.
Rolf-Erik Keck and Niklas Sondell
Wind Energ. Sci., 5, 997–1005,Short summary
A method for performing numerical wind resource assessments in the absence of on-site measurements is presented and validated against field measurements. Numerical wind resource assessment is at least 2 orders of magnitude faster and less expensive than using conventional site measurements. This enables analysis of a larger number of projects and thereby increases the chances of discovering the best available sites. The uncertainty in mean wind speed predictions is found to be about 4 %.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 5, 959–975,Short summary
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to mischaracterization of millions of dollars' worth of wind resource. This paper combines meteorological knowledge with machine learning techniques, specifically artificial neural networks (ANNs), to better extrapolate wind speeds. It is found that ANNs can reduce power-law extrapolation error by up to 52 % while simultaneously reducing uncertainty. A test case is shown to help decipher the ANN results.
Inga Reinwardt, Levin Schilling, Peter Dalhoff, Dirk Steudel, and Michael Breuer
Wind Energ. Sci., 5, 775–792,Short summary
This study presents a measurement campaign, which consists of two nacelle-mounted lidar systems in a densely packed onshore wind farm. The aim of the campaign is to validate and improve wake models for load and power estimations in wind farms. Based on the findings from the measurements, the formulation of the wake degradation in the dynamic wake meandering model has been adjusted, so that the recalibrated model coincides very well with the measurements and thereby reduces the uncertainties.
Ingrid Neunaber and Caroline Braud
Wind Energ. Sci., 5, 759–773,Short summary
Motivated by the need for wind turbine rotor blade tests in flows with atmospheric-like properties like gusts, we present a new setup to generate strong, rapid, turbulent gusts in a wind tunnel. The setup consists of a rotating bar that cuts through the inlet of the wind tunnel which generates the gust, and it is called
the chopper. In this work, the flow generated by the chopper is characterized, and we show how the gust and its turbulence evolve downstream.
Barthelmie, R., Frandsen, S., Rathmann, O., Hansen, K., Politis, E., Prospathopoulos, J., Schepers, J., Rados, K., Cabezon, D., Schlez, W., Neubert, A., and Heath, M.: Flow and Wakes in Large Wind Farms: Final Report for UPWIND WP8, Tech. Rep. Risø-R-1765 (EN), Risø, 2011. a
Beaucage, P., Brower, M., Robinson, N., and Alonge, C.: Overview of Six Commercial and Research Wake Models for Large Offshore Wind Farms, in: European Wind Energy Association Conference 2012, 2012. a
Bradstock, P., Schlez, W., Lindahl, S., and Schmidt, S.: Reduction of Wake Modelling Uncertainty Using a 3D RANS Model, in: WindEurope Global Wind Summit, Hamburg, Germany, available at: https://windeurope.org/summit2018/conference/proceedings/ (last access: 23 October 2020), 2018. a
Braunheim, F., Schlez, W., and Jothiprakasam, V.: Wind farm simulation and validation of analytical and CFD based Wake Models, in: WindEurope Global Wind Summit, Hamburg, Germany, 2018. a
Burden, R. L. and Faires, J. D.: Numerical Analysis. 2001, Brooks/Cole, USA, 2001. a
Businger, J. A.: Transfer of Heat and Momentum in the Atmospheric Boundary Layer, in: Proc. Arctic Heat Budget and Atmospheric Circulation, 1966. a
Businger, J. A.: Flux-Profile Relationships in the Atmospheric Surface Layer, J. Atmos. Sci., 28, 181–189, 1971. a
Crespo, A., Chacón, L., Hernández, J., Manuel, F., and Grau, J.: UPMPARK: a Parabolic 3D Code to Model Wind Farms, Proceedings of EWEC 1994, 1, 454–459, 1994. a
Crespo, A., Hernandez, J., and Frandsen, S.: Survey of Modelling Methods for Wind Turbine Wakes and Wind Farms, Wind Energy, 2, 1–24, 1999. a
Eecen, P. J., Wagenaar, J. W., and Bot, E. T. G.: Offshore Wind Farms: Losses and Turbulence in Wakes, Tech. Rep. ECN-M–11-065, ECN, Wake Workshop, 8–9 June, Gotland University, Sweden, available at: https://publications.tno.nl/publication/34631359/40Zi6N/m11065.pdf (last access: 23 October 2020), 2011. a
Ferziger, J. H. and Perić, M.: Computational Methods for Fluid Dynamics, 2nd ed., Springer Verlag, Berlin, Germany, ISBN 3-540-65373-2, 1999. a
Ishihara, T. and Qian, G.-W.: A new Gaussian-Based Analytical Wake Model for Wind Turbines Considering Ambient Turbulence Intensities and Thrust Coefficient Effects, J. Wind Eng. Ind. Aerod., 177, 275–292, 2018. a
Jensen, N.: A Note on Wind Generator Interaction, Tech. Rep. M-2411, Risø National Laboratory, Roskilde, Denmark, 1983. a
Liddell, A., Schlez, W., Neubert, A., Pena, A., and Trujillo, J.: Advanced Wake Model for Closely Spaced Turbines, in: (CD-ROM) Windpower 2005 Conference and Exhibition, Denver, Colorado, United States, 15–18 May 2005. a
Lissaman, P.: Energy Effectiveness of Arbitrary Arrays of Wind Turbines, AIAA Journal of Energy, New York, 3, 6, 1979. a
Ott, S.: Linearised CFD Models For Wakes, Tech. Rep. Risoe-R-1772(EN), Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi. Denmark, Forskningscenter Risoe, 2011. a
Peaceman, D. and Rachford, H.: The Numerical Solution of Parabolic and Elliptic Differential Equations, J. Soc. Indust. Appl. Math., 3, 28–41, 1955. a
ProPlanEn: WakeBlaster, ProPlanEn Ltd., available at: https://www.proplanen.info/wakeblaster, last access: 23 October 2020. a
Prospathopoulos, J. M., Rados, K. G., Cabezon, D., Schepers, J. G., Politis, E., Hansen, K., Chaviaropoulos, P. K., and Barthelmie, R. J.: Simulation of Wind Farms in Flat and Complex Terrain using CFD, in: Torque 2010: The Science of Making Torque from Wind, 359–370, 2010. a
Sanz, J., Borbon, F., Fernandes, P., and Garcia, B.: The OWA Wake Modelling Challenge Blind Tests, in: WindEurope Offshore 2019, Copenhagen, 28 November 2019. a
Schepers, J.: ENDOW: Validation and improvement of ECN's Wake Model, Tech. Rep. ECN-C-03-034, ECN, available at: https://repository.tudelft.nl/search/tno/ (last access: 23 October 2020), 2003. a
Schlez, W., Neubert, A., and Smith, G.: New Developments in Precision Wind Farm Modelling, in: DEWEK 2006, 22–23 November 2006, Bremen, Germany, 2006. a
Schlez, W., Neubert, A., and Prakesh, C.: New Developments in Large Wind Farm Modelling, in: European Wind Energy Conference and Exhibition 2009, Marseilles, , France, 16–19 March, 2, 1351–1373, available at: https://windeurope.org/members-area/events-networking/proceedings/ (last access: 23 October 2020), 2009. a
Schlez, W., Bradstock, P., Lindahl, S., and Tinning, M.: WakeBlaster- Understanding Wind Farm Performance, in: WindEurope Conference & Exhibition, 29–30 November 2017, Amsterdam, 2017a. a
Schlez, W., Bradstock, P., Tinning, M., and Lindahl, S.: Virtual Wind Farm Simulation A Closer Look at the WakeBlaster Project, WindTech International, 13, available at: https://www.windtech-international.com/editorial-features/a-closer-look-at-the-wakeblaster-project (last access: 23 October 2020), 2017b. a
Schlez, W., Bradstock, P., Tinning, M., and Lindahl, S.: Verification and Validation of a real-time CFD wake model for offshore wind farms, in: International Offshore Wind Partnering Forum, Princeton, NJ, 2018. a
Sørensen, N.: General purpose flow solver applied to flow over hills, Risø National Laboratory, Technical Report Risø-R-827, available at: https://orbit.dtu.dk/files/12280331/Ris_R_827.pdf (last access: 23 October 2020), 1995. a
Thomas, L.: Elliptic Problems in Linear Difference Equations Over a Network, Tech. rep., Waston Sci. Comput. Lab., Columbia University, New York, USA, 1949. a
Vermeer, L., Sørensen, J. N., and Crespo, A.: Wind Turbine Wake Aerodynamics, Prog. Aerosp. Sci., 39, 467–510, 2003. a
von Rosenberg, D. U.: Methods for the Numerical Solution of Partial Differential Equations, American Elsevier Publishing Company, Inc., New York, USA, 1983. a
The ProPlanEn team developed WakeBlaster, a new very fast numerical model for simulating the power output of wind farms. Accurate modelling of the waked flow enables the reduction of wind farm losses. By modelling the whole wind farm, WakeBlaster replaces simpler models which superimpose symmetrical solutions of the flow behind individual wind turbines. The paper describes the fundamental equations, discusses the scalability of the solution, and demonstrates the 3D flow on an offshore wind farm.
The ProPlanEn team developed WakeBlaster, a new very fast numerical model for simulating the...