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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.
This study presents a measurement campaign, which consists of two nacelle-mounted lidar systems...
WES | Articles | Volume 5, issue 2
Wind Energ. Sci., 5, 775–792, 2020
https://doi.org/10.5194/wes-5-775-2020
© 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
Wind Energ. Sci., 5, 775–792, 2020
https://doi.org/10.5194/wes-5-775-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article 19 Jun 2020
Research article | 19 Jun 2020
Dynamic wake meandering model calibration using nacelle-mounted lidar systems
Inga Reinwardt et al.
Related authors
Inga Reinwardt, Levin Schilling, Dirk Steudel, Nikolay Dimitrov, Peter Dalhoff, and Michael Breuer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-126, https://doi.org/10.5194/wes-2020-126, 2020
Revised manuscript accepted for WES
Short summary
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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.
Inga Reinwardt, Levin Schilling, Dirk Steudel, Nikolay Dimitrov, Peter Dalhoff, and Michael Breuer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-126, https://doi.org/10.5194/wes-2020-126, 2020
Revised manuscript accepted for WES
Short summary
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.
Sven Störtenbecker, Peter Dalhoff, Mukunda Tamang, and Rudolf Anselm
Wind Energ. Sci., 5, 1121–1128, https://doi.org/10.5194/wes-5-1121-2020, https://doi.org/10.5194/wes-5-1121-2020, 2020
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Multi-rotor wind turbine systems show the potential to reduce the levelized cost of energy. In this study a simplified and fast method as a first venture to find an optimal number of rotors and design parameters is presented. A variety of space frame designs are dimensioned based on ultimate loads and buckling, as a preliminary step for later detailed analyses.
Related subject area
Wind and turbulence
Set-point optimization in wind farms to mitigate effects of flow blockage induced by atmospheric gravity waves
Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy
Computational analysis of high-lift-generating airfoils for diffuser-augmented wind turbines
Aeroelastic analysis of wind turbines under turbulent inflow conditions
Parameterization of wind evolution using lidar
Mountain waves can impact wind power generation
Observations and simulations of a wind farm modifying a thunderstorm outflow boundary
The Alaiz experiment: untangling multi-scale stratified flows over complex terrain
Experimental and numerical simulation of extreme operational conditions for horizontal axis wind turbines based on the IEC standard
Validation of the dynamic wake meandering model with respect to loads and power production
Global trends in the performance of large wind farms based on high-fidelity simulations
The most similar predictor – on selecting measurement locations for wind resource assessment
Mitigation of offshore wind power intermittency by interconnection of production sites
Changing the rotational direction of a wind turbine under veering inflow: a parameter study
Optimal tuning of engineering wake models through lidar measurements
Evaluation of the lattice Boltzmann method for wind modelling in complex terrain
The digital terrain model in the computational modelling of the flow over the Perdigão site: the appropriate grid size
Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements
Theory and verification of a new 3D RANS wake model
Wake redirection at higher axial induction
Does 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 models
An alternative form of the super-Gaussian wind turbine wake model
Multipoint reconstruction of wind speeds
How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine
Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements
Multi-lidar wind resource mapping in complex terrain
Clustering wind profile shapes to estimate airborne wind energy production
Validation of Sentinel-1 offshore winds and average wind power estimation around Ireland
Validation of uncertainty reduction by using multiple transfer locations for WRF–CFD coupling in numerical wind energy assessments
Decreasing wind speed extrapolation error via domain-specific feature extraction and selection
An Overview of Wind Energy Production Prediction Bias, Losses, and Uncertainties
Axial induction controller field test at Sedini wind farm
First characterization of a new perturbation system for gust generation: the chopper
Utilizing Physics-Based Input Features within a Machine Learning Model to Predict Wind Speed Forecasting Error
Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar
The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds
Exploring the complexities associated with full-scale wind plant wake mitigation control experiments
Mitigating impact of spatial variance of turbulence in wind energy applications
Comparing abnormalities in onshore and offshore vertical wind profiles
Europe's offshore winds assessed with synthetic aperture radar, ASCAT and WRF
Rossby number similarity of an atmospheric RANS model using limited-length-scale turbulence closures extended to unstable stratification
Wind speed deviations in complex terrain
Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations
How to improve the state of the art in metocean measurement datasets
The Power Curve Working Group's assessment of wind turbine power performance prediction methods
Aeroelastic response of a multi-megawatt upwind horizontal axis wind turbine (HAWT) based on fluid–structure interaction simulation
Effect of tip spacing, thrust coefficient and turbine spacing in multi-rotor wind turbines and farms
Cluster wakes impact on a far-distant offshore wind farm's power
A double-multiple streamtube model for vertical axis wind turbines of arbitrary rotor loading
Luca Lanzilao and Johan Meyers
Wind Energ. Sci., 6, 247–271, https://doi.org/10.5194/wes-6-247-2021, https://doi.org/10.5194/wes-6-247-2021, 2021
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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.
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, https://doi.org/10.5194/wes-6-159-2021, https://doi.org/10.5194/wes-6-159-2021, 2021
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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, https://doi.org/10.5194/wes-6-149-2021, https://doi.org/10.5194/wes-6-149-2021, 2021
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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, https://doi.org/10.5194/wes-6-93-2021, https://doi.org/10.5194/wes-6-93-2021, 2021
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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, https://doi.org/10.5194/wes-6-61-2021, https://doi.org/10.5194/wes-6-61-2021, 2021
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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, https://doi.org/10.5194/wes-6-45-2021, https://doi.org/10.5194/wes-6-45-2021, 2021
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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, https://doi.org/10.5194/wes-6-1-2021, https://doi.org/10.5194/wes-6-1-2021, 2021
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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, https://doi.org/10.5194/wes-5-1793-2020, https://doi.org/10.5194/wes-5-1793-2020, 2020
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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, https://doi.org/10.5194/wes-5-1755-2020, https://doi.org/10.5194/wes-5-1755-2020, 2020
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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.
Inga Reinwardt, Levin Schilling, Dirk Steudel, Nikolay Dimitrov, Peter Dalhoff, and Michael Breuer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-126, https://doi.org/10.5194/wes-2020-126, 2020
Revised manuscript accepted for WES
Short summary
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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.
Søren Juhl Andersen, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1689–1703, https://doi.org/10.5194/wes-5-1689-2020, https://doi.org/10.5194/wes-5-1689-2020, 2020
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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, https://doi.org/10.5194/wes-5-1679-2020, https://doi.org/10.5194/wes-5-1679-2020, 2020
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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, https://doi.org/10.5194/wes-5-1663-2020, https://doi.org/10.5194/wes-5-1663-2020, 2020
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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, https://doi.org/10.5194/wes-5-1623-2020, https://doi.org/10.5194/wes-5-1623-2020, 2020
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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, https://doi.org/10.5194/wes-5-1601-2020, https://doi.org/10.5194/wes-5-1601-2020, 2020
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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.
Alain Schubiger, Sarah Barber, and Henrik Nordborg
Wind Energ. Sci., 5, 1507–1519, https://doi.org/10.5194/wes-5-1507-2020, https://doi.org/10.5194/wes-5-1507-2020, 2020
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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, https://doi.org/10.5194/wes-5-1469-2020, https://doi.org/10.5194/wes-5-1469-2020, 2020
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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, https://doi.org/10.5194/wes-5-1449-2020, https://doi.org/10.5194/wes-5-1449-2020, 2020
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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.
Philip Bradstock and Wolfgang Schlez
Wind Energ. Sci., 5, 1425–1434, https://doi.org/10.5194/wes-5-1425-2020, https://doi.org/10.5194/wes-5-1425-2020, 2020
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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.
Carlo Cossu
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-111, https://doi.org/10.5194/wes-2020-111, 2020
Revised manuscript accepted for WES
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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 result confirm in a realistic setting 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 gains enhancements are obtained by means of overinductive yaw control.
Antonia Englberger, Andreas Dörnbrack, and Julie K. Lundquist
Wind Energ. Sci., 5, 1359–1374, https://doi.org/10.5194/wes-5-1359-2020, https://doi.org/10.5194/wes-5-1359-2020, 2020
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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, https://doi.org/10.5194/wes-5-1253-2020, https://doi.org/10.5194/wes-5-1253-2020, 2020
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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.
Frédéric Blondel and Marie Cathelain
Wind Energ. Sci., 5, 1225–1236, https://doi.org/10.5194/wes-5-1225-2020, https://doi.org/10.5194/wes-5-1225-2020, 2020
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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, https://doi.org/10.5194/wes-5-1211-2020, https://doi.org/10.5194/wes-5-1211-2020, 2020
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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.
Patrick Murphy, Julie K. Lundquist, and Paul Fleming
Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, https://doi.org/10.5194/wes-5-1169-2020, 2020
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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, https://doi.org/10.5194/wes-5-1129-2020, https://doi.org/10.5194/wes-5-1129-2020, 2020
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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, https://doi.org/10.5194/wes-5-1059-2020, https://doi.org/10.5194/wes-5-1059-2020, 2020
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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, https://doi.org/10.5194/wes-5-1097-2020, https://doi.org/10.5194/wes-5-1097-2020, 2020
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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, https://doi.org/10.5194/wes-5-1023-2020, https://doi.org/10.5194/wes-5-1023-2020, 2020
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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, https://doi.org/10.5194/wes-5-997-2020, https://doi.org/10.5194/wes-5-997-2020, 2020
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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, https://doi.org/10.5194/wes-5-959-2020, https://doi.org/10.5194/wes-5-959-2020, 2020
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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.
Joseph C. Y. Lee and M. Jason Fields
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-85, https://doi.org/10.5194/wes-2020-85, 2020
Revised manuscript accepted for WES
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This overview 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 calls for further improvements on the prediction methodologies and more observations for validation.
Ervin A. Bossanyi and Renzo Ruisi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-88, https://doi.org/10.5194/wes-2020-88, 2020
Revised manuscript accepted for WES
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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 have demonstrated that this goal has been achieved, also providing convincing validation of the model used for designing the controller.
Ingrid Neunaber and Caroline Braud
Wind Energ. Sci., 5, 759–773, https://doi.org/10.5194/wes-5-759-2020, https://doi.org/10.5194/wes-5-759-2020, 2020
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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.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-61, https://doi.org/10.5194/wes-2020-61, 2020
Revised manuscript accepted for WES
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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, an artificial neural network (ANN), in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as ANN inputs in an effort to discern the most useful atmospheric information for this purpose.
Felix Kelberlau and Jakob Mann
Wind Energ. Sci., 5, 519–541, https://doi.org/10.5194/wes-5-519-2020, https://doi.org/10.5194/wes-5-519-2020, 2020
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Wind speeds can be measured remotely from the ground with lidars. Their estimates are accurate for mean speeds, but turbulence leads to measurement errors. We predict these errors using computer-generated data and compare lidar measurements with data from a meteorological mast. The comparison shows that deviations depend on wind direction, measurement height, and wind conditions. Our method to reduce the measurement error is successful when the wind is aligned with one of the lidar beams.
Nicola Bodini and Mike Optis
Wind Energ. Sci., 5, 489–501, https://doi.org/10.5194/wes-5-489-2020, https://doi.org/10.5194/wes-5-489-2020, 2020
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An accurate assessment of the wind resource at hub height is necessary for an efficient and bankable wind farm project. Conventional techniques for wind speed vertical extrapolation include a power law and a logarithmic law. Here, we propose a round-robin validation to assess the benefits that a machine-learning-based approach can provide in vertically extrapolating wind speed at a location different from the training site – the most practically useful application for the wind energy industry.
James B. Duncan Jr., Brian D. Hirth, and John L. Schroeder
Wind Energ. Sci., 5, 469–488, https://doi.org/10.5194/wes-5-469-2020, https://doi.org/10.5194/wes-5-469-2020, 2020
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Results highlight some of the complexities associated with executing and analyzing wind plant control at full scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes on smaller timescales, (2) identifying reliable data sources and methods to quantify these control changes, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions.
Jonas Kazda and Jakob Mann
Wind Energ. Sci., 5, 439–450, https://doi.org/10.5194/wes-5-439-2020, https://doi.org/10.5194/wes-5-439-2020, 2020
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This work presents the first analytical solution for the quantification of the spatial variance of the second-order moment of correlated wind speeds. The spatial variance is defined as random differences in the sample variance of wind speed between different points in space. The approach is successfully verified using simulation and field data. The impact of the spatial variance on wind farm control, the verification of wind turbine performance and sensor verification are then investigated.
Mathias Møller, Piotr Domagalski, and Lars Roar Sætran
Wind Energ. Sci., 5, 391–411, https://doi.org/10.5194/wes-5-391-2020, https://doi.org/10.5194/wes-5-391-2020, 2020
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This work has analyzed historical data of 10 min averaged wind speed measurements to investigate the accuracy of the commonly used equations for describing the wind velocity as a function of the height above ground. The results of analyzing data from a wide range of sites show that the common equations do not sufficiently describe certain physical phenomena, especially at offshore and coastal locations. The results imply that there is a need for more advanced models.
Charlotte B. Hasager, Andrea N. Hahmann, Tobias Ahsbahs, Ioanna Karagali, Tija Sile, Merete Badger, and Jakob Mann
Wind Energ. Sci., 5, 375–390, https://doi.org/10.5194/wes-5-375-2020, https://doi.org/10.5194/wes-5-375-2020, 2020
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Europe's offshore wind resource mapping is part of the New European Wind Atlas (NEWA) international consortium effort. This study presents the results of analysis of synthetic aperture radar (SAR) ocean wind maps based on Envisat and Sentinel-1 with a brief description of the wind retrieval process and Advanced Scatterometer (ASCAT) ocean wind maps. Furthermore, the Weather Research and Forecasting (WRF) offshore wind atlas of NEWA is presented.
Maarten Paul van der Laan, Mark Kelly, Rogier Floors, and Alfredo Peña
Wind Energ. Sci., 5, 355–374, https://doi.org/10.5194/wes-5-355-2020, https://doi.org/10.5194/wes-5-355-2020, 2020
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The design of wind turbines and wind farms can be improved by increasing the accuracy of the inflow models representing the atmospheric boundary layer (ABL). In this work we employ numerical simulations of the idealized ABL, which can represent the mean effects of Coriolis and buoyancy forces and surface roughness. We find a new model-based similarity that provides a better understanding of the idealized ABL. In addition, we extend the model to include effects of convective buoyancy forces.
Christian Ingenhorst, Georg Jacobs, Laura Stößel, Ralf Schelenz, and Björn Juretzki
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-25, https://doi.org/10.5194/wes-2020-25, 2020
Revised manuscript accepted for WES
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Wind farm sites within 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 mean wind speed of approx. ± 30 %, indicating the importance of an extensive site evaluation to reduce investment risk.
Paul Hulsman, Søren Juhl Andersen, and Tuhfe Göçmen
Wind Energ. Sci., 5, 309–329, https://doi.org/10.5194/wes-5-309-2020, https://doi.org/10.5194/wes-5-309-2020, 2020
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We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion, are built using high-fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°.
Erik Quaeghebeur and Michiel B. Zaaijer
Wind Energ. Sci., 5, 285–308, https://doi.org/10.5194/wes-5-285-2020, https://doi.org/10.5194/wes-5-285-2020, 2020
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Meteorological and oceanic datasets are fundamental to the modeling of offshore wind farms. Data quality issues in one such dataset led us to conduct a study to establish whether such issues are more generally present in these datasets. The answer is yes and users should be aware of this. We therefore also investigated how such issues can be avoided. The result is a set of techniques and recommendations for dataset producers, leading to substantial quality improvements with limited extra effort.
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, https://doi.org/10.5194/wes-5-199-2020, 2020
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This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
Yasir Shkara, Martin Cardaun, Ralf Schelenz, and Georg Jacobs
Wind Energ. Sci., 5, 141–154, https://doi.org/10.5194/wes-5-141-2020, https://doi.org/10.5194/wes-5-141-2020, 2020
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A computational fluid dynamics (CFD) solver is coupled with a structure solver to predict the dynamic response of a horizontal axis wind turbine structure. CFD provides much more accurate and more realistic aerodynamic loads that cannot be achieved by traditional methods such as blade element momentum theory. As a result, the aeroelastic response of the wind turbine structure, taking into account blade–tower interactions, is described in more detail.
Niranjan S. Ghaisas, Aditya S. Ghate, and Sanjiva K. Lele
Wind Energ. Sci., 5, 51–72, https://doi.org/10.5194/wes-5-51-2020, https://doi.org/10.5194/wes-5-51-2020, 2020
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Wakes of a multi-rotor wind turbine configuration are evaluated using numerical simulations. Compared to equivalent conventional single-rotor turbine wakes, multi-rotor turbine wakes are found to recover faster and generate less turbulence; thus, multi-rotor turbine wind farms are more efficient, with smaller wake losses. The benefits of multi-rotor wind farms over conventional wind farms are sensitive to tip spacing, thrust coefficient and turbine spacing.
Jörge Schneemann, Andreas Rott, Martin Dörenkämper, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 5, 29–49, https://doi.org/10.5194/wes-5-29-2020, https://doi.org/10.5194/wes-5-29-2020, 2020
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Offshore wind farm clusters cause reduced wind speeds in downstream regions which can extend over more than 50 km.
We analysed the impact of these so-called cluster wakes on a distant wind farm using remote-sensing wind measurements and power production data.
Cluster wakes caused power losses up to 55 km downstream in certain atmospheric states.
A better understanding of cluster wake effects reduces uncertainties in offshore wind resource assessment and improves offshore areal planning.
Anis A. Ayati, Konstantinos Steiros, Mark A. Miller, Subrahmanyam Duvvuri, and Marcus Hultmark
Wind Energ. Sci., 4, 653–662, https://doi.org/10.5194/wes-4-653-2019, https://doi.org/10.5194/wes-4-653-2019, 2019
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This work presents an improvement to a popular algorithm used in the prediction of the power of vertical axis wind turbines. While, until now, the algorithm was considered valid only for low rotor speeds and a small number of blades, our improvement makes it valid for any configuration. The predictions of the improved algorithm were found to be of considerable accuracy, when validated with measurements of a small model turbine acquired in a compressed air wind tunnel.
Cited articles
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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.
This study presents a measurement campaign, which consists of two nacelle-mounted lidar systems...
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