Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-959-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-5-959-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jul 2020
Research article |
| 26 Jul 2020
| 26 Jul 2020
Decreasing wind speed extrapolation error via domain-specific feature extraction and selection
Daniel Vassallo
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
Raghavendra Krishnamurthy
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
Pacific Northwest National Laboratory, Washington, USA
Harindra J. S. Fernando
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
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Wei Fu, Alfredo Peña, and Jakob Mann
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Mads Baungaard, Maarten Paul van der Laan, and Mark Kelly
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Oliver Maas and Siegfried Raasch
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Wind Energ. Sci., 7, 659–676, https://doi.org/10.5194/wes-7-659-2022, https://doi.org/10.5194/wes-7-659-2022, 2022
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Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 7, 539–558, https://doi.org/10.5194/wes-7-539-2022, https://doi.org/10.5194/wes-7-539-2022, 2022
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Lidar-assisted control of wind turbines requires a wind field generator capable of simulating wind evolution. Out of this need, we extend the Veers method for 3D wind field generation to 4D and propose a two-step Cholesky decomposition approach. Based on this, we develop a 4D wind field generator – evoTurb – coupled with TurbSim and Mann turbulence generator. We further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide practical suggestions.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
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Tobias Klaas-Witt and Stefan Emeis
Wind Energ. Sci., 7, 413–431, https://doi.org/10.5194/wes-7-413-2022, https://doi.org/10.5194/wes-7-413-2022, 2022
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Light detection and ranging (lidar) has become a valuable technology to assess the wind resource at hub height of modern wind turbines. However, because of their measurement principle, common lidars suffer from errors at orographically complex, i.e. hilly or mountainous, sites. This study analyses the impact of the five main influencing factors in a non-dimensional, model-based parameter study.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
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Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
Sonja Krüger, Gerald Steinfeld, Martin Kraft, and Laura J. Lukassen
Wind Energ. Sci., 7, 323–344, https://doi.org/10.5194/wes-7-323-2022, https://doi.org/10.5194/wes-7-323-2022, 2022
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Detailed numerical simulations of turbines in atmospheric conditions are challenging with regard to their computational demand. We coupled an atmospheric flow model and a turbine model in order to deliver extensive details about the flow and the turbine response within reasonable computational time. A comparison to measurement data was performed and showed a very good agreement. The efficiency of the tool enables applications such as load calculation in wind farms or during low-level-jet events.
Michael F. Howland, Aditya S. Ghate, Jesús Bas Quesada, Juan José Pena Martínez, Wei Zhong, Felipe Palou Larrañaga, Sanjiva K. Lele, and John O. Dabiri
Wind Energ. Sci., 7, 345–365, https://doi.org/10.5194/wes-7-345-2022, https://doi.org/10.5194/wes-7-345-2022, 2022
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Wake steering control, in which turbines are intentionally misaligned with the incident wind, has demonstrated potential to increase wind farm energy. We investigate wake steering control methods in simulations of a wind farm operating in the terrestrial diurnal cycle. We develop a statistical wind direction forecast to improve wake steering in flows with time-varying states. Closed-loop wake steering control increases wind farm energy production, compared to baseline and open-loop control.
Paul Hulsman, Martin Wosnik, Vlaho Petrović, Michael Hölling, and Martin Kühn
Wind Energ. Sci., 7, 237–257, https://doi.org/10.5194/wes-7-237-2022, https://doi.org/10.5194/wes-7-237-2022, 2022
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Due to the possibility of mapping the wake fast at multiple locations with the WindScanner, a thorough understanding of the development of the wake is acquired at different inflow conditions and operational conditions. The lidar velocity data and the energy dissipation rate compared favourably with hot-wire data from previous experiments, lending credibility to the measurement technique and methodology used here. This will aid the process to further improve existing wake models.
Ingrid Neunaber, Joachim Peinke, and Martin Obligado
Wind Energ. Sci., 7, 201–219, https://doi.org/10.5194/wes-7-201-2022, https://doi.org/10.5194/wes-7-201-2022, 2022
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Wind turbines are often clustered within wind farms. A consequence is that some wind turbines may be exposed to the wakes of other turbines, which reduces their lifetime due to the wake turbulence. Knowledge of the wake is thus important, and we carried out wind tunnel experiments to investigate the wakes. We show how models that describe wakes of bluff bodies can help to improve the understanding of wind turbine wakes and wind turbine wake models, particularly by including a virtual origin.
Elena Cantero, Javier Sanz, Fernando Borbón, Daniel Paredes, and Almudena García
Wind Energ. Sci., 7, 221–235, https://doi.org/10.5194/wes-7-221-2022, https://doi.org/10.5194/wes-7-221-2022, 2022
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The impact of atmospheric stability on wind energy is widely demonstrated, so we have to know how to characterise it.
This work based on a meteorological mast located in a complex terrain compares and evaluates different instrument set-ups and methodologies for stability characterisation. The methods are examined considering their theoretical background, implementation complexity, instrumentation requirements and practical use in connection with wind energy applications.
Peter Brugger, Corey Markfort, and Fernando Porté-Agel
Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, https://doi.org/10.5194/wes-7-185-2022, 2022
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Wind turbines create a wake of reduced wind speeds downstream of the rotor. The wake does not necessarily have a straight, pencil-like shape but can meander similar to a smoke plume. We investigated this wake meandering and observed that the downstream transport velocity is slower than the wind speed contrary to previous assumptions and that the evolution of the atmospheric turbulence over time impacts wake meandering on distances typical for the turbine spacing in wind farms.
Ida Marie Solbrekke, Asgeir Sorteberg, and Hilde Haakenstad
Wind Energ. Sci., 6, 1501–1519, https://doi.org/10.5194/wes-6-1501-2021, https://doi.org/10.5194/wes-6-1501-2021, 2021
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We validate new high-resolution data set (NORA3) for offshore wind power purposes for the North Sea and the Norwegian Sea. The aim of the validation is to ensure that NORA3 can act as a wind resource data set in the planning phase for future offshore wind power installations in the area of concern. The general conclusion of the validation is that NORA3 is well suited for wind power estimates but gives slightly conservative estimates of the offshore wind metrics.
Liang Dong, Wai Hou Lio, and Eric Simley
Wind Energ. Sci., 6, 1491–1500, https://doi.org/10.5194/wes-6-1491-2021, https://doi.org/10.5194/wes-6-1491-2021, 2021
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This paper suggests that the impacts of different turbulence models should be considered as uncertainties while evaluating the benefits of lidar-assisted control (LAC) in wind turbine design. The value creation of LAC, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors.
Alexander Basse, Doron Callies, Anselm Grötzner, and Lukas Pauscher
Wind Energ. Sci., 6, 1473–1490, https://doi.org/10.5194/wes-6-1473-2021, https://doi.org/10.5194/wes-6-1473-2021, 2021
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This study investigates systematic, seasonal biases in the long-term correction of short-term wind measurements (< 1 year). Two popular measure–correlate–predict (MCP) methods yield remarkably different results. Six reanalysis data sets serve as long-term data. Besides experimental results, theoretical findings are presented which link the mechanics of the methods and the properties of the reanalysis data sets to the observations. Finally, recommendations for wind park planners are derived.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
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This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Rogier Floors, Merete Badger, Ib Troen, Kenneth Grogan, and Finn-Hendrik Permien
Wind Energ. Sci., 6, 1379–1400, https://doi.org/10.5194/wes-6-1379-2021, https://doi.org/10.5194/wes-6-1379-2021, 2021
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Wind turbines are frequently placed in forests. We investigate the potential of using satellites to characterize the land surface for wind flow modelling. Maps of forest properties are generated from satellite data and converted to flow modelling maps. Validation is carried out at 10 sites. Using the novel satellite-based maps leads to lower errors of the power density than land cover databases, which demonstrates the value of using satellite-based land cover maps for flow modelling.
Christoffer Hallgren, Stefan Ivanell, Heiner Körnich, Ville Vakkari, and Erik Sahlée
Wind Energ. Sci., 6, 1205–1226, https://doi.org/10.5194/wes-6-1205-2021, https://doi.org/10.5194/wes-6-1205-2021, 2021
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As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods is evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend selecting which method to use based on the forecasted weather.
Mark Kelly, Søren Juhl Andersen, and Ásta Hannesdóttir
Wind Energ. Sci., 6, 1227–1245, https://doi.org/10.5194/wes-6-1227-2021, https://doi.org/10.5194/wes-6-1227-2021, 2021
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Via 11 years of measurements, we made a representative ensemble of wind ramps in terms of acceleration, mean speed, and shear. Constrained turbulence and large-eddy simulations were coupled to an aeroelastic model for each ensemble member. Ramp acceleration was found to dominate the maxima of thrust-associated loads, with a ramp-induced increase of 45 %–50 % plus ~ 3 % per 0.1 m/s2 of bulk ramp acceleration magnitude. The LES indicates that the ramps (and such loads) persist through the farm.
Davide Conti, Nikolay Dimitrov, Alfredo Peña, and Thomas Herges
Wind Energ. Sci., 6, 1117–1142, https://doi.org/10.5194/wes-6-1117-2021, https://doi.org/10.5194/wes-6-1117-2021, 2021
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We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.
Tanvi Gupta and Somnath Baidya Roy
Wind Energ. Sci., 6, 1089–1106, https://doi.org/10.5194/wes-6-1089-2021, https://doi.org/10.5194/wes-6-1089-2021, 2021
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Wind turbines extract momentum from atmospheric flow and convert that to electricity. This study explores recovery processes in wind farms that replenish the momentum so that wind farms can continue to function. Experiments with a numerical model show that momentum transport by turbulent eddies from above the wind turbines is the major contributor to recovery except for strong wind conditions and low wind turbine density, where horizontal advection can also play a major role.
Mithu Debnath, Paula Doubrawa, Mike Optis, Patrick Hawbecker, and Nicola Bodini
Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, https://doi.org/10.5194/wes-6-1043-2021, 2021
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As the offshore wind industry emerges on the US East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. We leverage a year of data of two floating lidars to quantify and characterize the frequent occurrence of high-wind-shear and low-level-jet events, both of which will have a considerable impact on turbine operation. We find that almost 100 independent long events occur throughout the year.
Jeanie A. Aird, Rebecca J. Barthelmie, Tristan J. Shepherd, and Sara C. Pryor
Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021, https://doi.org/10.5194/wes-6-1015-2021, 2021
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Low-level jets (LLJs) are pronounced maxima in wind speed profiles affecting wind turbine performance and longevity. We present a climatology of LLJs over Iowa using output from the Weather Research and Forecasting (WRF) model and determine the rotor plane conditions when they occur. LLJ characteristics are highly sensitive to the identification criteria applied, and different (unique) LLJs are extracted with each criterion. LLJ characteristics also vary with different model output resolution.
Janna Kristina Seifert, Martin Kraft, Martin Kühn, and Laura J. Lukassen
Wind Energ. Sci., 6, 997–1014, https://doi.org/10.5194/wes-6-997-2021, https://doi.org/10.5194/wes-6-997-2021, 2021
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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 depends on their location within 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.
Mike Optis, Nicola Bodini, Mithu Debnath, and Paula Doubrawa
Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, https://doi.org/10.5194/wes-6-935-2021, 2021
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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, https://doi.org/10.5194/wes-6-841-2021, https://doi.org/10.5194/wes-6-841-2021, 2021
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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, https://doi.org/10.5194/wes-6-777-2021, https://doi.org/10.5194/wes-6-777-2021, 2021
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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, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
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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.
Carlo Cossu
Wind Energ. Sci., 6, 663–675, https://doi.org/10.5194/wes-6-663-2021, https://doi.org/10.5194/wes-6-663-2021, 2021
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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, https://doi.org/10.5194/wes-6-645-2021, https://doi.org/10.5194/wes-6-645-2021, 2021
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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, https://doi.org/10.5194/wes-6-627-2021, https://doi.org/10.5194/wes-6-627-2021, 2021
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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, https://doi.org/10.5194/wes-6-521-2021, https://doi.org/10.5194/wes-6-521-2021, 2021
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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, https://doi.org/10.5194/wes-6-505-2021, https://doi.org/10.5194/wes-6-505-2021, 2021
Kamran Shirzadeh, Horia Hangan, Curran Crawford, and Pooyan Hashemi Tari
Wind Energ. Sci., 6, 477–489, https://doi.org/10.5194/wes-6-477-2021, https://doi.org/10.5194/wes-6-477-2021, 2021
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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, https://doi.org/10.5194/wes-6-441-2021, https://doi.org/10.5194/wes-6-441-2021, 2021
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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.
Christian Ingenhorst, Georg Jacobs, Laura Stößel, Ralf Schelenz, and Björn Juretzki
Wind Energ. Sci., 6, 427–440, https://doi.org/10.5194/wes-6-427-2021, https://doi.org/10.5194/wes-6-427-2021, 2021
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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, https://doi.org/10.5194/wes-6-389-2021, https://doi.org/10.5194/wes-6-389-2021, 2021
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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 indicate that this goal has been achieved, also providing convincing validation of the model used for designing the controller.
Carlo Cossu
Wind Energ. Sci., 6, 377–388, https://doi.org/10.5194/wes-6-377-2021, https://doi.org/10.5194/wes-6-377-2021, 2021
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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 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, https://doi.org/10.5194/wes-6-311-2021, https://doi.org/10.5194/wes-6-311-2021, 2021
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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, https://doi.org/10.5194/wes-6-295-2021, https://doi.org/10.5194/wes-6-295-2021, 2021
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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, 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, 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.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: Tensorflow: Large-scale machine learning on heterogeneous distributed systems, arXiv preprint: arXiv:1603.04467, 2016. a
Akish, E., Bianco, L., Djalalova, I. V., Wilczak, J. M., Olson, J. B., Freedman, J., Finley, C., and Cline, J.: Measuring the impact of additional instrumentation on the skill of numerical weather prediction models at forecasting wind ramp events during the first Wind Forecast Improvement Project (WFIP), Wind Energy, 22, 1165–1174, 2019. a
A2e – Atmosphere to Electrons: wfip2/lidar.z03.00, Maintained by A2e Data Archive and Portal for US Department of Energy, Office of Energy Efficiency and Renewable Energy, https://doi.org/10.21947/1328914, 2017. a
Baklanov, A. A., Grisogono, B., Bornstein, R., Mahrt, L., Zilitinkevich, S. S., Taylor, P., Larsen, S. E., Rotach, M. W., and Fernando, H.: The nature,
theory, and modeling of atmospheric planetary boundary layers, B. Am. Meteorol. Soc., 92, 123–128, 2011. a
Berg, L. K., Liu, Y., Yang, B., Qian, Y., Olson, J., Pekour, M., Ma, P.-L., and Hou, Z.: Sensitivity of Turbine-Height Wind Speeds to Parameters in the
Planetary Boundary-Layer Parametrization Used in the Weather Research and
Forecasting Model: Extension to Wintertime Conditions, Bound.-Lay. Meteorol., 170, 507–518, 2019. a
Bianco, L., Djalalova, I. V., Wilczak, J. M., Olson, J. B., Kenyon, J. S., Choukulkar, A., Berg, L. K., Fernando, H. J. S., Grimit, E. P., Krishnamurthy, R., Lundquist, J. K., Muradyan, P., Pekour, M., Pichugina, Y., Stoelinga, M. T., and Turner, D. D.: Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2), Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, 2019. a
Bilgili, M., Sahin, B., and Yasar, A.: Application of artificial neural
networks for the wind speed prediction of target station using reference
stations data, Renew. Energy, 32, 2350–2360, 2007. a
Bodini, N., Lundquist, J. K., Krishnamurthy, R., Pekour, M., Berg, L. K., and Choukulkar, A.: Spatial and temporal variability of turbulence dissipation rate in complex terrain, Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019, 2019. a
Chen, Y., Zhang, S., Zhang, W., Peng, J., and Cai, Y.: Multifactor
spatio-temporal correlation model based on a combination of convolutional
neural network and long short-term memory neural network for wind speed
forecasting, Energy Convvers. Manage., 185, 783–799, 2019. a
Chollet, F., Falbel, D., Allaire, J., Tang, Y., Van Der Bijl, W., Studer, M., and Keydana, S.: Keras, available at: https://github.com/fchollet/keras (last access: 10 February 2019), 2015. a
Dupré, A., Drobinski, P., Alonzo, B., Badosa, J., Briard, C., and
Plougonven, R.: Sub-hourly forecasting of wind speed and wind energy, Renew. Energy, 145, 2373–2379, 2020. a
Fernando, H. J. S., Pardyjak, E. R., Di Sabatino, S., Chow, F. K., De Wekker, S. F. J., Hoch, S. W., Hacker, J., Pace, J. C., Pratt, T., Pu, Z., Steenburgh, W. J., Whiteman, C. D., Wang, Y., Zajic, D., Balsley, B., Dimitrova, R., Emmitt, G. D., Higgins, C. W., Hunt, J. C. R., Knievel, J. C., Lawrence, D., Liu, Y., Nadeau, D. F., Kit, E., Blomquist, B. W., Conry, P., Coppersmith, R. S., Creegan, E., Felton, M., Grachev, A., Gunawardena, N., Hang, C., Hocut, C. M., Huynh, G., Jeglum, M. E., Jensen, D., Kulandaivelu, V., Lehner, M., Leo, L. S., Liberzon, D., Massey, J. D., McEnerney, K., Pal, S., Price, T., Sghiatti, M., Silver, Z., Thompson, M., Zhang, H., and Zsedrovits, T.: The MATERHORN: Unravelingthe intricacies of mountain weather, B. Am. Meteorol. Soc., 96, 1945–1967, 2015. a, b
Fernando, H. J. S., Mann, J., Palma, J. M. L. M., Lundquist, J. K., Barthelmie, R. J., Belo-Pereira, M., Brown, W. O. J., Chow, F. K., Gerz, T., Hocut, C. M., Klein, P. M., Leo, L. S., Matos, J. C., Oncley, S. P., Pryor, S. C., Bariteau, L., Bell, T. M., Bodini, N., Carney, M. B., Courtney, M. S., Creegan, E. D., Dimitrova, R., Gomes, S., Hagen, M., Hyde, J. O., Kigle, S., Krishnamurthy, R., Lopes, J. C., Mazzaro, L., Neher, J. M. T., Menke, R., Murphy, P., Oswald, L., Otarola-Bustos, S., Pattantyus, A. K., Viega Rodrigues, C., Schady, A., Sirin, N., Spuler, S., Svensson, E., Tomaszewski, J., Turner, D. D., van Veen, L., Vasiljević, N., Vassallo, D., Voss, S., Wildmann, N., and Wang, Y.: The Perdigao: Peering into microscale details of mountain winds, B. Am. Meteorol. Soc., 100, 799–819, 2019. a, b
Fleming, P., King, J., Dykes, K., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Lopez, H., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1, Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, 2019. a
Fleming, P. A., Scholbrock, A., Jehu, A., Davoust, S., Osler, E., Wright,
A. D., and Clifton, A.: Field-test results using a nacelle-mounted lidar for
improving wind turbine power capture by reducing yaw misalignment, J. Phys.: Conf. Ser., 524, 012002, https://doi.org/10.1088/1742-6596/524/1/012002, 2014. a
Garratt, J.: The internal boundary layer – a review, Bound.-Lay. Meteorol., 50, 171–203, 1990. a
Giyanani, A., Bierbooms, W., and van Bussel, G.: Lidar uncertainty and beam averaging correction, Adv. Sci. Res., 12, 85–89, https://doi.org/10.5194/asr-12-85-2015, 2015. a
Holmes, J. D.: Wind loading of structures, CRC Press, Boca Raton, FL, USA, 2018. a
Hsieh, W. W. and Tang, B.: Applying neural network models to prediction and
data analysis in meteorology and oceanography, B. Am. Meteorol. Soc., 79, 1855–1870, 1998. a
Islam, M. S., Mohandes, M., and Rehman, S.: Vertical extrapolation of wind
speed using artificial neural network hybrid system, Neural Comput. Appl., 28, 2351–2361, 2017. a
Kim, D., Kim, T., Oh, G., Huh, J., and Ko, K.: A comparison of ground-based
LiDAR and met mast wind measurements for wind resource assessment over
various terrain conditions, J. Wind Eng. Indust. Aerodynam., 158, 109–121, 2016. a
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv
preprint: arXiv:1412.6980, 2014. a
Krishnamurthy, R.: Wind farm characterization and control using coherent
Doppler lidar, PhD thesis, Arizona State University, Arizona, 2013. a
Krishnamurthy, R., Calhoun, R., Billings, B., and Doyle, J. D.: Mesoscale model evaluation with coherent Doppler lidar for wind farm assessment, Remote
Sens. Lett., 4, 579–588, 2013. a
Kumar, A. A., Bossanyi, E. A., Scholbrock, A. K., Fleming, P., Boquet, M., and Krishnamurthy, R.: Field Testing of LIDAR-Assisted Feedforward Control
Algorithms for Improved Speed Control and Fatigue Load Reduction on a 600-kW
Wind Turbine, Tech. rep., NREL – National Renewable Energy Lab., Golden, CO, USA, 2015. a
Li, F., Ren, G., and Lee, J.: Multi-step wind speed prediction based on
turbulence intensity and hybrid deep neural networks, Energy Convers. Manage., 186, 306–322, 2019. a
Lundquist, J.: Lidar-CU WindCube V1 Profiler, Wasco Airport-Reviewed Data,
Tech. rep., Atmosphere to Electrons (A2e) Data Archive and Portal, PNNL – Pacific Northwest National Laboratory, available at: https://a2e.energy.gov/data/wfip2/lidar.z03.b0 (last access: 16 June 2020), 2017. a
Lundquist, J. K., Churchfield, M. J., Lee, S., and Clifton, A.: Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics, Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, 2015. a
Lundquist, J. K., Wilczak, J. M., Ashton, R., Bianco, L., Brewer, W. A., Choukulkar, A., Clifton, A., Debnath, M., Delgado, R., Friedrich, K., Gunter, S., Hamidi, A., Iungo, G. V., Kaushik, A., Kosović, B., Langan, P., Lass, A., Lavin, E., Lee, J. C. Y., McCaffrey, K. L., Newsom, R. K., Noone, D. C., Oncley, S. P., Quelet, P. T., Sandberg, S. P., Schroeder, J. L., Shaw, W. J., Sparling, L., St. Martin, C., St. Pe, A., Strobach, E., Tay, K., Vanderwende, B. J., Weickmann, A., Wolfe, D., and Worsnop, R.: Assessing state-of-the-art capabilities for probing the atmospheric boundary layer: the XPIA field campaign, B. Am. Meteorol. Soc., 98, 289–314, 2017. a
Markou, H. and Larsen, T. J.: Control strategies for operation of pitch
regulated turbines above cut-out wind speeds, in: 2009 European Wind Energy
Conference and Exhibition, EWEC, 16–19 March 2009, Parc Chanot, Marseille, France, 2009. a
McGovern, A., Lagerquist, R., John Gagne, D., Jergensen, G. E., Elmore, K. L., Homeyer, C. R., and Smith, T.: Making the black box more transparent:
Understanding the physical implications of machine learning, B. Am. Meteorol. Soc., 100, 2175–2199, 2019. a
More, A. and Deo, M.: Forecasting wind with neural networks, Mar. Struct., 16, 35–49, 2003. a
Newman, J. F. and Clifton, A.: Moving Beyond 2 % Uncertainty: A New Framework for Quantifying Lidar Uncertainty, Tech. rep., NREL – National Renewable Energy Lab. Golden, CO, USA, 2017. a
Newsom, R. K., Brewer, W. A., Wilczak, J. M., Wolfe, D. E., Oncley, S. P., and Lundquist, J. K.: Validating precision estimates in horizontal wind measurements from a Doppler lidar, Atmos. Meas. Tech., 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, 2017. a
Olson, J., Kenyon, J. S., Djalalova, I., Bianco, L., Turner, D. D., Pichugina, Y., Choukulkar, A., Toy, M. D., Brown, J. M., Angevine, W. M., Akish, E., Bao, J. W., Jimenez, P., Kosovic, B., Lundquist, K. A., Draxl, C., Lundquist, J. K., McCaa, J., McCaffrey, K., Lantz, K., Long, C., Wilczak, J., Banta, R., Marquis, M., Redfern, S., Berg, L. K., Shaw, W., and Cline, J.: Improving wind energy forecasting through numerical weather prediction model development, B. Am. Meteorol. Soc., 100, 2201–2220, 2019. a
Parish, T. R., Rahn, D. A., and Leon, D.: Airborne observations of a Catalina
eddy, Mon. Weather Rev., 141, 3300–3313, 2013. a
Pichugina, Y. L., Banta, R. M., Bonin, T., Brewer, W. A., Choukulkar, A., McCarty, B. J., Baidar, S., Draxl, C., Fernando, H. J. S., Kenyon, J., Krishnamurthy, R., Marquis, M., Olson, J., Sharp, J., and Stoelinga, M.: Spatial Variability of Winds and HRRR-NCEP Model Error Statistics at Three Doppler-Lidar Sites in the Wind-Energy Generation Region of the Columbia River Basin, J. Appl. Meteorol. Clim., 58.8, 1633–1656, 2019. a
Schlipf, D., Schlipf, D. J., and Kühn, M.: Nonlinear model predictive
control of wind turbines using LIDAR, Wind Energy, 16, 1107–1129, 2013. a
Shu, Z., Li, Q., He, Y., and Chan, P.: Observations of offshore wind
characteristics by Doppler-LiDAR for wind energy applications, Appl. Energy, 169, 150–163, 2016. a
Türkan, Y. S., Aydoğmuş, H. Y., and Erdal, H.: The prediction
of the wind speed at different heights by machine learning methods,
Int. J. Optimiz. Control: Theor. Appl., 6, 179–187, 2016. a
UCAR/NCAR Earth Observing Laboratory: Perdigão Field Experiment Data, available at: https://data.eol.ucar.edu/project/Perdigao, last access: 10 January 2019. a
Vassallo, D.: Wind Speed Extrapolation Codes, available at: https://github.com/dvassall/Wind-Speed-Extrapolation-Codes, last access: 16 July 2019. a
Vassallo, D., Krishnamurthy, R., and Fernando, H. J. S.: Utilizing Physics-Based Input Features within a Machine Learning Model to Predict Wind Speed Forecasting Error, Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-61, in review, 2020. a
Wang, Q., Alappattu, D. P., Billingsley, S., Blomquist, B., Burkholder, R. J., Christman, A. J., Creegan, E. D., de Paolo, T., Eleuterio, D. P., Fernando, H. J. S., Franklin, K. B., Grachev, A. A., Haack, T., Hanley, T. R., Hocut, C. M., Holt, T. R., Horgan, K., Jonsson, H. H., Hale, R. A., Kalogiros, J. A., Khelif, D., Leo, L. S., Lind, R. J., Lozovatsky, I., Planella-Morato, J., Mukherjee, S., Nuss, W. A., Pozderac, J., Rogers, L. T., Savelyev, I., Savidge, D. K., Shearman, R. K., Shen, L., Terrill, E., Ulate, A. M., Wang, Q., Wendt, R. T., Wiss, R., Woods, R. K., Xu, L., Yamaguchi, R. T., and Yardim, C.: CASPER: Coupled Air–Sea Processes and Electromagnetic Ducting Research, B. Am. Meteorol. Soc., 99, 1449–1471, 2018. a
Wharton, S. and Lundquist, J. K.: Atmospheric stability affects wind turbine
power collection, Environ. Res. Lett., 7, 014005, https://doi.org/10.1088/1748-9326/7/1/014005, 2012. a
Wilczak, J. M., Stoelinga, M., Berg, L. K., Sharp, J., Draxl, C., McCaffrey, K., Banta, R. M., Bianco, L., Djalalova, I., Lundquist, J. K., Muradyan, P., Choukulkar, A., Leo, L., Bonin, T., Pichugina, Y., Eckman, R., Long, C. N., Lantz, K., Worsnop, R. P., Bickford, J., Bodini, N., Chand, D., Clifton, A., Cline, J., Cook, D. R., Fernando, H. J. S., Friedrich, K., Krishnamurthy, R., Marquis, M., McCaa, J., Olson, J. B., Otarola-Bustos, S., Scott, G., Shaw, W. J., Wharton, S., and White, A. B.: The Second Wind Forecast Improvement Project (WFIP2): Observational Field Campaign, B. Am. Meteorol. Soc., 100.9, 1701–1723, 2019. a, b
Yang, B., Qian, Y., Berg, L. K., Ma, P. L., Wharton, S., Bulaevskaya, V., Yan, H., Hou, Z., and Shaw, W. J.: Sensitivity of turbine-height wind speeds to parameters in planetary boundary-layer and surface-layer schemes in the
weather research and forecasting model, Bound.-Lay. Meteorol., 162, 117–142, 2017. a
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.
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to...
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