Articles | Volume 7, issue 5
https://doi.org/10.5194/wes-7-2135-2022
© Author(s) 2022. 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-7-2135-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Oct 2022
Research article |
| 26 Oct 2022
| 26 Oct 2022
Gaussian mixture model for extreme wind turbulence estimation
Technical University of Denmark, Department of Wind and Energy Systems, Frederiksborgvej 399, 4000 Roskilde, Denmark
Anand Natarajan
Technical University of Denmark, Department of Wind and Energy Systems, Frederiksborgvej 399, 4000 Roskilde, Denmark
Related authors
Xiaodong Zhang and Nikolay Dimitrov
Wind Energ. Sci., 8, 1613–1623, https://doi.org/10.5194/wes-8-1613-2023, https://doi.org/10.5194/wes-8-1613-2023, 2023
Short summary
Short summary
Wind turbine extreme response estimation based on statistical extrapolation necessitates using a small number of simulations to calculate a low exceedance probability. This is a challenging task especially if we require small prediction error. We propose the use of a Gaussian mixture model as it is capable of estimating a low exceedance probability with minor bias error, even with limited simulation data, having flexibility in modeling the distributions of varying response variables.
Xiaodong Zhang and Nikolay Dimitrov
Wind Energ. Sci., 8, 1613–1623, https://doi.org/10.5194/wes-8-1613-2023, https://doi.org/10.5194/wes-8-1613-2023, 2023
Short summary
Short summary
Wind turbine extreme response estimation based on statistical extrapolation necessitates using a small number of simulations to calculate a low exceedance probability. This is a challenging task especially if we require small prediction error. We propose the use of a Gaussian mixture model as it is capable of estimating a low exceedance probability with minor bias error, even with limited simulation data, having flexibility in modeling the distributions of varying response variables.
Anand Natarajan
Wind Energ. Sci., 7, 1171–1181, https://doi.org/10.5194/wes-7-1171-2022, https://doi.org/10.5194/wes-7-1171-2022, 2022
Short summary
Short summary
The article delineates a novel procedure to use 10 min measurement statistics with few known parameters of the wind turbine to determine the long-term fatigue damage probability and compare this with the expected damage levels from the design to provide an indicator of structural reliability and remaining life. The results are validated with load measurements from a wind turbine within an offshore wind farm.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Wind and turbulence
TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows
Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States
The wind farm pressure field
Offshore low-level jet observations and model representation using lidar buoy data off the California coast
Realistic turbulent inflow conditions for estimating the performance of a floating wind turbine
Evaluation of wind farm parameterizations in the WRF model under different atmospheric stability conditions with high-resolution wake simulations
Brief communication: On the definition of the low-level jet
A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset
Revealing inflow and wake conditions of a 6 MW floating turbine
Machine Learning Methods to Improve Spatial Predictions of Coastal Wind Speed Profiles and Low-Level Jets using Single-Level ERA5 Data
Control-Oriented Modelling of Wind Direction Variability
The fractal turbulent/non-turbulent interface in the atmosphere
Measurement-Driven Large-Eddy Simulations of a Diurnal Cycle during a Wake Steering Field Campaign
Renewable Energy Complementarity (RECom) Maps – a comprehensive visualisation tool to support spatial diversification
Stochastic gradient descent for wind farm optimization
Modelling the impact of trapped lee waves on offshore wind farm power output
Applying a random time mapping to Mann-modeled turbulence for the generation of intermittent wind fields
From shear to veer: theory, statistics, and practical application
Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines
Gaussian mixture models for the optimal sparse sampling of offshore wind resource
Dependence of turbulence estimations on nacelle lidar scanning strategies
Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques
An investigation of spatial wind direction variability and its consideration in engineering models
From gigawatt to multi-gigawatt wind farms: wake effects, energy budgets and inertial gravity waves investigated by large-eddy simulations
Investigations of correlation and coherence in turbulence from a large-eddy simulation
Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast
On the laminar–turbulent transition mechanism on megawatt wind turbine blades operating in atmospheric flow
Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model
Turbulence structures and entrainment length scales in large offshore wind farms
Effect of different source terms and inflow direction in atmospheric boundary modeling over the complex terrain site of Perdigão
Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm
Adjusted spectral correction method for calculating extreme winds in tropical-cyclone-affected water areas
The Jensen wind farm parameterization
Current and future wind energy resources in the North Sea according to CMIP6
Optimization of wind farm portfolios for minimizing overall power fluctuations at selective frequencies – a case study of the Faroe Islands
Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe
The sensitivity of the Fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme
Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast
Statistical post-processing of reanalysis wind speeds at hub heights using a diagnostic wind model and neural networks
Turbulence in a coastal environment: the case of Vindeby
Computational-fluid-dynamics analysis of a Darrieus vertical-axis wind turbine installation on the rooftop of buildings under turbulent-inflow conditions
Spatiotemporal observations of nocturnal low-level jets and impacts on wind power production
Computational fluid dynamics studies on wind turbine interactions with the turbulent local flow field influenced by complex topography and thermal stratification
Brief communication: How does complex terrain change the power curve of a wind turbine?
The wide range of factors contributing to wind resource assessment accuracy in complex terrain
High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning
Impact of the wind field at the complex-terrain site Perdigão on the surface pressure fluctuations of a wind turbine
Surrogate models for the blade element momentum aerodynamic model using non-intrusive polynomial chaos expansions
Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling
Classification and properties of non-idealized coastal wind profiles – an observational study
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, https://doi.org/10.5194/wes-9-297-2024, 2024
Short summary
Short summary
In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
Rebecca Foody, Jacob Coburn, Jeanie A. Aird, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 9, 263–280, https://doi.org/10.5194/wes-9-263-2024, https://doi.org/10.5194/wes-9-263-2024, 2024
Short summary
Short summary
Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore locations, we quantify the advantages of deploying wind turbines offshore in terms of the amount of electrical power produced and the higher reliability and predictability of the electrical power.
Ronald B. Smith
Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, https://doi.org/10.5194/wes-9-253-2024, 2024
Short summary
Short summary
Recent papers have investigated the impact of turbine drag on local wind patterns, but these studies have not given a full explanation of the induced pressure field. The pressure field blocks and deflects the wind and in other ways modifies farm efficiency. Current gravity wave models are complex and provide no estimation tools. We dig deeper into the cause of the pressure field and provide approximate closed-form expressions for pressure field effects.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-152, https://doi.org/10.5194/wes-2023-152, 2023
Revised manuscript under review for WES
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the U.S. Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Cédric Raibaudo, Jean-Christophe Gilloteaux, and Laurent Perret
Wind Energ. Sci., 8, 1711–1725, https://doi.org/10.5194/wes-8-1711-2023, https://doi.org/10.5194/wes-8-1711-2023, 2023
Short summary
Short summary
The work presented here proposes interfacing experimental measurements performed in a wind tunnel with simulations conducted with the aeroelastic code FAST and applied to a floating wind turbine model under wave-induced motion. FAST simulations using experiments match well with those obtained using the inflow generation method provided by TurbSim. The highest surge motion frequencies show a significant decrease in the mean power produced by the turbine and a mitigation of the flow dynamics.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-124, https://doi.org/10.5194/wes-2023-124, 2023
Revised manuscript under review for WES
Short summary
Short summary
We studied how good the representation of wind farms in weather models is by comparing two popular and simpler methods with a detailed simulation. Our results showed that while these simple methods can predict certain aspects well, they have limitations in specific scenarios. For instance, they can accurately predict wind speed changes in certain areas but might struggle in others. Ultimately, our goal was to make wind farm predictions more reliable and beneficial for the energy sector.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
Short summary
Short summary
Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Serkan Kartal, Sukanta Basu, and Simon J. Watson
Wind Energ. Sci., 8, 1533–1551, https://doi.org/10.5194/wes-8-1533-2023, https://doi.org/10.5194/wes-8-1533-2023, 2023
Short summary
Short summary
Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.
Nikolas Angelou, Jakob Mann, and Camille Dubreuil-Boisclair
Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, https://doi.org/10.5194/wes-8-1511-2023, 2023
Short summary
Short summary
This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-122, https://doi.org/10.5194/wes-2023-122, 2023
Revised manuscript under review for WES
Short summary
Short summary
To know the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle capturing both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the northern hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Scott Dallas, Adam Stock, and Edward Hart
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-93, https://doi.org/10.5194/wes-2023-93, 2023
Revised manuscript accepted for WES
Short summary
Short summary
This review presents the current understanding of wind direction variability in the context of control-oriented modelling of wind turbines and wind farms in a manner suitable to a wide audience. Motivation comes from the significant and commonly seen yaw error of horizontal axis wind turbines, which carries substantial negative impacts on annual energy production and the levelised cost of wind energy. Gaps in the literature are identified and the critical challenges in this area are discussed.
Lars Neuhaus, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-73, https://doi.org/10.5194/wes-2023-73, 2023
Revised manuscript accepted for WES
Short summary
Short summary
Future wind turbines reach unprecedented heights and are affected by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions with a turbulent/non-turbulent interface (TNTI) becomes more likely. In this paper, the presence and fractality of this TNTI in the atmosphere are studied. Typical fractalities known from ideal laboratory and numerical studies and a frequent occurrence of the TNTI at heights of multi-megawatt turbines are found.
Eliot Quon
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-101, https://doi.org/10.5194/wes-2023-101, 2023
Revised manuscript accepted for WES
Short summary
Short summary
Engineering models used to design wind farms generally do not account for aerodynamic interactions between turbines, turbine wakes, and the atmosphere over time. This paper uses a first-principles simulation technique to predict the performance of 5 turbines within an operational wind farm during a wake steering experiment. Challenges included limited atmospheric data and atypical conditions. The simulation was able to capture wake-induced flow speedup and the corresponding power increase.
Til Kristian Vrana and Harald G. Svendsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-76, https://doi.org/10.5194/wes-2023-76, 2023
Revised manuscript accepted for WES
Short summary
Short summary
We developed new ways to plot nice and comprehensive wind resource maps that show the revenue potential of different locations for future wind power developments. The relative capacity factor is introduced as an indicator showing the expected mean power output. The market value factor is introduced, that captures the expected mean market value relative to other wind parks. The RECom index combines the two into a single index, resulting in the RECom map.
Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller
Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, https://doi.org/10.5194/wes-8-1235-2023, 2023
Short summary
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Sarah J. Ollier and Simon J. Watson
Wind Energ. Sci., 8, 1179–1200, https://doi.org/10.5194/wes-8-1179-2023, https://doi.org/10.5194/wes-8-1179-2023, 2023
Short summary
Short summary
This modelling study shows that topographic trapped lee waves (TLWs) modify flow behaviour and power output in offshore wind farms. We demonstrate that TLWs can substantially alter the wind speeds at individual wind turbines and effect the power output of the turbine and whole wind farm. The impact on wind speeds and power is dependent on which part of the TLW wave cycle interacts with the wind turbines and wind farm. Positive and negative impacts of TLWs on power output are observed.
Khaled Yassin, Arne Helms, Daniela Moreno, Hassan Kassem, Leo Höning, and Laura J. Lukassen
Wind Energ. Sci., 8, 1133–1152, https://doi.org/10.5194/wes-8-1133-2023, https://doi.org/10.5194/wes-8-1133-2023, 2023
Short summary
Short summary
The current turbulent wind field models stated in the IEC 61400-1 standard underestimate the probability of extreme changes in wind velocity. This underestimation can lead to the false calculation of extreme and fatigue loads on the turbine. In this work, we are trying to apply a random time-mapping technique to one of the standard turbulence models to adapt to such extreme changes. The turbulent fields generated are compared with a standard wind field to show the effects of this new mapping.
Mark Kelly and Maarten Paul van der Laan
Wind Energ. Sci., 8, 975–998, https://doi.org/10.5194/wes-8-975-2023, https://doi.org/10.5194/wes-8-975-2023, 2023
Short summary
Short summary
The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot
Wind Energ. Sci., 8, 771–786, https://doi.org/10.5194/wes-8-771-2023, https://doi.org/10.5194/wes-8-771-2023, 2023
Short summary
Short summary
A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
Short summary
Short summary
Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Daniel Hatfield, Charlotte Bay Hasager, and Ioanna Karagali
Wind Energ. Sci., 8, 621–637, https://doi.org/10.5194/wes-8-621-2023, https://doi.org/10.5194/wes-8-621-2023, 2023
Short summary
Short summary
Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods, yet their temporal resolution is limited and they are only representative at 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant to wind energy purposes.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Oliver Maas
Wind Energ. Sci., 8, 535–556, https://doi.org/10.5194/wes-8-535-2023, https://doi.org/10.5194/wes-8-535-2023, 2023
Short summary
Short summary
The study compares small vs. large wind farms regarding the flow and power output with a turbulence-resolving simulation model. It shows that a large wind farm (90 km length) significantly affects the wind direction and that the wind speed is higher in the large wind farm wake. Both wind farms excite atmospheric gravity waves that also affect the power output of the wind farms.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
Short summary
Short summary
Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci., 8, 303–326, https://doi.org/10.5194/wes-8-303-2023, https://doi.org/10.5194/wes-8-303-2023, 2023
Short summary
Short summary
Results from the DAN-AERO and aerodynamic glove projects provide significant findings. The effects of inflow turbulence on transition and wind turbine blades are compared to computational fluid dynamic simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of amplification factor is evident from the simulations. An agreement between the power spectral density plots from the experiment and large-eddy simulations is seen.
Frédéric Blondel
Wind Energ. Sci., 8, 141–147, https://doi.org/10.5194/wes-8-141-2023, https://doi.org/10.5194/wes-8-141-2023, 2023
Short summary
Short summary
Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. Wake superposition methods play a key role and remain a substantial source of uncertainty. In the present work, a momentum-conserving superposition method is extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.
Abdul Haseeb Syed, Jakob Mann, Andreas Platis, and Jens Bange
Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, https://doi.org/10.5194/wes-8-125-2023, 2023
Short summary
Short summary
Wind turbines extract energy from the incoming wind flow, which needs to be recovered. In very large offshore wind farms, the energy is recovered mostly from above the wind farm in a process called entrainment. In this study, we analyzed the effect of atmospheric stability on the entrainment process in large offshore wind farms using measurements recorded by a research aircraft. This is the first time that in situ measurements are used to study the energy recovery process above wind farms.
Kartik Venkatraman, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Teigen Giljarhus
Wind Energ. Sci., 8, 85–108, https://doi.org/10.5194/wes-8-85-2023, https://doi.org/10.5194/wes-8-85-2023, 2023
Short summary
Short summary
This paper is focused on the impact of modeling different effects, such as forest canopy and Coriolis forces, on the wind resource over a complex terrain site located near Perdigão, Portugal. A numerical model is set up and results are compared with field measurements. The results show that including a forest canopy improves the predictions close to the ground at some locations on the site, while the model with inflow from a precursor performed better at other locations.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
Short summary
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Xiaoli Guo Larsén and Søren Ott
Wind Energ. Sci., 7, 2457–2468, https://doi.org/10.5194/wes-7-2457-2022, https://doi.org/10.5194/wes-7-2457-2022, 2022
Short summary
Short summary
A method is developed for calculating the extreme wind in tropical-cyclone-affected water areas. The method is based on the spectral correction method that fills in the missing wind variability to the modeled time series, guided by best track data. The paper provides a detailed recipe for applying the method and the 50-year winds of equivalent 10 min temporal resolution from 10 to 150 m in several tropical-cyclone-affected regions.
Yulong Ma, Cristina L. Archer, and Ahmadreza Vasel-Be-Hagh
Wind Energ. Sci., 7, 2407–2431, https://doi.org/10.5194/wes-7-2407-2022, https://doi.org/10.5194/wes-7-2407-2022, 2022
Short summary
Short summary
Wind turbine wakes are important because they reduce the power production of wind farms and may cause unintended impacts on the weather around wind farms. Weather prediction models, like WRF and MPAS, are often used to predict both power and impacts of wind farms, but they lack an accurate treatment of wind farm wakes. We developed the Jensen wind farm parameterization, based on the existing Jensen model of an idealized wake. The Jensen parameterization is accurate and computationally efficient.
Andrea N. Hahmann, Oscar García-Santiago, and Alfredo Peña
Wind Energ. Sci., 7, 2373–2391, https://doi.org/10.5194/wes-7-2373-2022, https://doi.org/10.5194/wes-7-2373-2022, 2022
Short summary
Short summary
We explore the changes in wind energy resources in northern Europe using output from simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. Our results show that climate change does not particularly alter annual energy production in the North Sea but could affect the seasonal distribution of these resources, significantly reducing energy production during the summer from 2031 to 2050.
Turið Poulsen, Bárður A. Niclasen, Gregor Giebel, and Hans Georg Beyer
Wind Energ. Sci., 7, 2335–2350, https://doi.org/10.5194/wes-7-2335-2022, https://doi.org/10.5194/wes-7-2335-2022, 2022
Short summary
Short summary
Wind power is cheap and environmentally friendly, but it has a disadvantage: it is a variable power source. Because wind is not blowing everywhere simultaneously, optimal placement of wind farms can reduce the fluctuations.
This is explored for a small isolated area. Combining wind farms reduces wind power fluctuations for timescales up to 1–2 d. By optimally placing four wind farms, the hourly fluctuations are reduced by 15 %. These wind farms are located distant from each other.
Graziela Luzia, Andrea N. Hahmann, and Matti Juhani Koivisto
Wind Energ. Sci., 7, 2255–2270, https://doi.org/10.5194/wes-7-2255-2022, https://doi.org/10.5194/wes-7-2255-2022, 2022
Short summary
Short summary
This paper presents a comprehensive validation of time series produced by a mesoscale numerical weather model, a global reanalysis, and a wind atlas against observations by using a set of metrics that we present as requirements for wind energy integration studies. We perform a sensitivity analysis on the numerical weather model in multiple configurations, such as related to model grid spacing and nesting arrangements, to define the model setup that outperforms in various time series aspects.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
Short summary
Short summary
Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
Short summary
Short summary
Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Sebastian Brune and Jan D. Keller
Wind Energ. Sci., 7, 1905–1918, https://doi.org/10.5194/wes-7-1905-2022, https://doi.org/10.5194/wes-7-1905-2022, 2022
Short summary
Short summary
A post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8. The statistical correction using a neural network based on different variables of the reanalysis leads to an improvement of 30 % in RMSE compared to COSMO-REA6.
Rieska Mawarni Putri, Etienne Cheynet, Charlotte Obhrai, and Jasna Bogunovic Jakobsen
Wind Energ. Sci., 7, 1693–1710, https://doi.org/10.5194/wes-7-1693-2022, https://doi.org/10.5194/wes-7-1693-2022, 2022
Short summary
Short summary
As offshore wind turbines' sizes are increasing, thorough knowledge of wind characteristics in the marine atmospheric boundary layer (MABL) is becoming crucial to help improve offshore wind turbine design and reliability. The present study discusses the wind characteristics at the first offshore wind farm, Vindeby, and compares them with the wind measurements at the FINO1 platform. Consistent wind characteristics are found between Vindeby measurements and the FINO1 measurements.
Pradip Zamre and Thorsten Lutz
Wind Energ. Sci., 7, 1661–1677, https://doi.org/10.5194/wes-7-1661-2022, https://doi.org/10.5194/wes-7-1661-2022, 2022
Short summary
Short summary
To get more insight into the influence of the urban-terrain flow on the performance of the rooftop-mounted two-bladed Darrieus vertical-axis wind turbine, scale resolving simulations are performed for a generic wind turbine in realistic terrain under turbulent conditions. It is found that the turbulence and skewed nature of the flow near rooftop locations have a positive impact on the performance of the wind turbine.
Eduardo Weide Luiz and Stephanie Fiedler
Wind Energ. Sci., 7, 1575–1591, https://doi.org/10.5194/wes-7-1575-2022, https://doi.org/10.5194/wes-7-1575-2022, 2022
Short summary
Short summary
This work analyses a meteorological event, called nocturnal low-level jets (NLLJs), defined as high wind speeds relatively close to the surface. There were positive and negative impacts from NLLJs. While NLLJs increased the mean power production, they also increased the variability in the wind with height. Our results imply that long NLLJ events are also larger, affecting many wind turbines at the same time. Short NLLJ events are more local, having stronger effects on power variability.
Patrick Letzgus, Giorgia Guma, and Thorsten Lutz
Wind Energ. Sci., 7, 1551–1573, https://doi.org/10.5194/wes-7-1551-2022, https://doi.org/10.5194/wes-7-1551-2022, 2022
Short summary
Short summary
The research article presents the results of a study of highly resolved numerical simulations of a wind energy test site in complex terrain that is currently under construction in the Swabian Alps in southern Germany. The numerical results emphasised the importance of considering orography, vegetation, and thermal stratification in numerical simulations to resolve the wind field decently. In this way, the effects on loads, power, and wake of the wind turbine can also be predicted well.
Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, https://doi.org/10.5194/wes-7-1527-2022, 2022
Short summary
Short summary
This article shows that the power performance of a wind turbine may be very different in flat and complex terrain. This is an important finding because it shows that the power output of a given wind turbine is governed by not only the available wind at the position of the turbine but also how the ambient flow develops in the region behind the turbine.
Sarah Barber, Alain Schubiger, Sara Koller, Dominik Eggli, Alexander Radi, Andreas Rumpf, and Hermann Knaus
Wind Energ. Sci., 7, 1503–1525, https://doi.org/10.5194/wes-7-1503-2022, https://doi.org/10.5194/wes-7-1503-2022, 2022
Short summary
Short summary
In this work, a range of simulations are carried out with seven different wind modelling tools at five different complex terrain sites and the results compared to wind speed measurements at validation locations. This is then extended to annual energy production (AEP) estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. It is therefore vital to consider overall AEP when evaluating simulation accuracies.
Louis de Montera, Henrick Berger, Romain Husson, Pascal Appelghem, Laurent Guerlou, and Mauricio Fragoso
Wind Energ. Sci., 7, 1441–1453, https://doi.org/10.5194/wes-7-1441-2022, https://doi.org/10.5194/wes-7-1441-2022, 2022
Short summary
Short summary
A novel method for estimating offshore wind resources at turbine hub height with synthetic aperture radar (SAR) satellites is presented. The machine learning algorithm uses as input geometrical parameters of the SAR sensors and parameters related to atmospheric stability. It is trained with Doppler wind lidar vertical profiles. The extractable wind power accuracy up to 200 m is within 3 %, and SAR can resolve the coastal wind gradient, unlike the Weather Research and Forecasting numerical mode.
Florian Wenz, Judith Langner, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 7, 1321–1340, https://doi.org/10.5194/wes-7-1321-2022, https://doi.org/10.5194/wes-7-1321-2022, 2022
Short summary
Short summary
To get a better understanding of the influence of the terrain flow on the unsteady pressure distributions on the wind turbine surface, a fully resolved turbine was simulated in the complex terrain of Perdigão, Portugal. It was found that the pressure fluctuations at the tower caused by vortex shedding are significantly hampered by the terrain flow, while the pressure fluctuations caused by the blade–tower interaction are hardly changed.
Rad Haghi and Curran Crawford
Wind Energ. Sci., 7, 1289–1304, https://doi.org/10.5194/wes-7-1289-2022, https://doi.org/10.5194/wes-7-1289-2022, 2022
Short summary
Short summary
Based on the IEC standards, a limited number of simulations is sufficient to calculate the extreme and fatigue loads on a wind turbine. However, this means inaccuracy in the output statistics. This paper aims to build a surrogate model on blade element momentum aerodynamic model simulation output employing non-intrusive polynomial chaos expansion. The surrogate model is then used in a large number of Monte Carlo simulations to provide an accurate statistical estimate of the loads.
Beatriz Cañadillas, Maximilian Beckenbauer, Juan J. Trujillo, Martin Dörenkämper, Richard Foreman, Thomas Neumann, and Astrid Lampert
Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, https://doi.org/10.5194/wes-7-1241-2022, 2022
Short summary
Short summary
Scanning lidar measurements combined with meteorological sensors and mesoscale simulations reveal the strong directional and stability dependence of the wake strength in the direct vicinity of wind farm clusters.
Christoffer Hallgren, Johan Arnqvist, Erik Nilsson, Stefan Ivanell, Metodija Shapkalijevski, August Thomasson, Heidi Pettersson, and Erik Sahlée
Wind Energ. Sci., 7, 1183–1207, https://doi.org/10.5194/wes-7-1183-2022, https://doi.org/10.5194/wes-7-1183-2022, 2022
Short summary
Short summary
Non-idealized wind profiles with negative shear in part of the profile (e.g., low-level jets) frequently occur in coastal environments and are important to take into consideration for offshore wind power. Using observations from a coastal site in the Baltic Sea, we analyze in which meteorological and sea state conditions these profiles occur and study how they alter the turbulence structure of the boundary layer compared to idealized profiles.
Cited articles
Abdallah, I.: Assessment of extreme design loads for modern wind turbines using the probabilistic approach, DTU Wind Energy, ISBN 8793278322, ISBN 9788793278325, 2015. a
Abdallah, I., Natarajan, A., and Sørensen, J. D.: Influence of the control
system on wind turbine loads during power production in extreme turbulence:
Structural reliability, Renew. Energy, 87, 464–477,
https://doi.org/10.1016/j.renene.2015.10.044, 2016. a
Akaike, H.: Information theory and an extension of the maximum likelihood
principle, in: Selected papers of hirotugu akaike, Springer, New York, 199–213, https://doi.org/10.1007/978-1-4612-1694-0_15, 1998. a
Arthur, D. and Vassilvitskii, S.: k-means
Bouyé, E., Durrleman, V., Nikeghbali, A., Riboulet, G., and Roncalli, T.:
Copulas for finance – a reading guide and some applications, SSRN Electron. J., https://doi.org/10.2139/ssrn.1032533, 2011. a
Chang, G. W., Lu, H. J., Wang, P. K., Chang, Y. R., and Lee, Y. D.: Gaussian
mixture model-based neural network for short-term wind power forecast, Int. T. Elect. Energ. Syst., 27, e2320, https://doi.org/10.1002/etep.2320, 2017. a
Cui, M., Feng, C., Wang, Z., and Zhang, J.: Statistical representation of wind power ramps using a generalized Gaussian mixture model, IEEE T. Sustain. Energ., 9, 261–272, https://doi.org/10.1109/TSTE.2017.2727321, 2018. a
Dempster, A. P., Laird, N. M., and Rubin, D. B.: Maximum likelihood from
incomplete data via the EM algorithm, J. Roy. Stat. Soc. Ser. B, 39, 1–38,
1977. a
Dimitrov, N. K., Natarajan, A., and Mann, J.: Effects of normal and extreme
turbulence spectral parameters on wind turbine loads, Renew. Energy, 101,
1180–1193, https://doi.org/10.1016/j.renene.2016.10.001, 2017. a, b
Hannesdóttir, Á., Kelly, M., and Dimitrov, N.: Extreme wind
fluctuations: Joint statistics, extreme turbulence, and impact on wind
turbine loads, Wind Energ. Sci., 4, 325–342, https://doi.org/10.5194/wes-4-325-2019, 2019. a, b
Janouek, J., Gajdo, P., Radecky, M., and Snasel, V.: Gaussian mixture model
cluster forest, in: Proceedings – 2015 IEEE 14th International Conference on
Machine Learning and Applications, Icmla, Miami, Florida, USA, 1019–1023, https://doi.org/10.1109/ICMLA.2015.12, 2015. a
Li, T., Wang, Y., and Zhang, N.: Combining probability density forecasts for
power electrical loads, IEEE T. Smart Grid, 11, 1679–1690,
https://doi.org/10.1109/TSG.2019.2942024, 2020. a
Low, Y. M.: A new distribution for fitting four moments and its applications to reliability analysis, Struct. Safe., 42, 12–25,
https://doi.org/10.1016/j.strusafe.2013.01.007, 2013. a
Mann, J.: The spatial structure of neutral atmospheric surface-layer
turbulence, J. Fluid Mech., 273, 141–168, https://doi.org/10.1017/S0022112094001886, 1994. a
McLachlan, G. J., Lee, S. X., and Rathnayake, S. I.: Finite mixture models, Annu. Rev. Stat. Appl., 6, 355–378, https://doi.org/10.1146/annurev-statistics-031017-100325, 2019. a, b
Miyazaki, B., Izumi, K., Toriumi, F., and Takahashi, R.: Change detection of
orders in stock markets using a Gaussian mixture model, Intel. Syst. Account. Financ. Manage., 21, 169–191, https://doi.org/10.1002/isaf.1356, 2014. a
Monahan, A. H.: Idealized models of the joint probability distribution of wind speeds, Nonlin. Processes Geophys., 25, 335–353,
https://doi.org/10.5194/npg-25-335-2018, 2018. a
Peña Diaz, A., Floors, R. R., Sathe, A., Gryning, S.-E., Wagner, R., Courtney, M., Larsén, X. G., Hahmann, A. N., and Hasager, C. B.: Ten years of boundary-layer and wind-power meteorology at Høvsøre, Denmark, Bound.-Lay. Meteorol., 158, 1–26, https://doi.org/10.1007/s10546-015-0079-8, 2016. a
Permuter, H., Francos, J., and Jermyn, I. H.: Gaussian mixture models of
texture and colour for image database retrieval, in: Icassp, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings, 3, 6–10 April 2003, Hong Kong, China, 569–572, https://doi.org/10.1109/ICASSP.2003.1199538, 2003. a
Prabakaran, I., Wu, Z., Lee, C., Tong, B., Steeman, S., Koo, G., Zhang, P. J., and Guvakova, M. A.: Gaussian mixture models for probabilistic classification of breast cancer, Cancer Res., 79, 3492–3502,
https://doi.org/10.1158/0008-5472.CAN-19-0573, 2019.
a
Reynolds, D. and Rose, R.: Robust text-independent speaker identification using Gaussian mixture speaker models, IEEE T. Speech Audio Process., 3, 72–83, https://doi.org/10.1109/89.365379, 1995. a
Schwarz, G.: Estimating the dimension of a model, Ann. Stat., 6, 461–464, https://doi.org/10.1214/aos/1176344136, 1978. a
Srbinovski, B., Temko, A., Leahy, P., Pakrashi, V., and Popovici, E.: Gaussian mixture models for site-specific wind turbine power curves, Proc. Inst. Mech. Eng. Pt. A, 235, 494–505, https://doi.org/10.1177/0957650920931729, 2021. a
Steinhoff, C., Müller, T., Nuber, U. A., and Vingron, M.: Gaussian mixture density estimation applied to microarray data, Lect. Notes Comput. Sci., 2810, 418–429, https://doi.org/10.1007/978-3-540-45231-7_39, 2003. a, b
Wahbah, M., Alhussein, O., El-Fouly, T. H., Zahawi, B., and Muhaidat, S.:
Evaluation of parametric statistical models for wind speed probability
density estimation, in: 2018 IEEE Electrical Power and Energy Conference, Epec 2018, Toronto, Ontario, 8598283, https://doi.org/10.1109/EPEC.2018.8598283, 2018. a
Xiao, Q.: Evaluating correlation coefficient for Nataf transformation,
Probabil. Eng. Mech., 37, 1–6, https://doi.org/10.1016/j.probengmech.2014.03.010, 2014. a, b
Zhang, J., Yan, J., Infield, D., Liu, Y., and sang Lien, F.: Short-term
forecasting and uncertainty analysis of wind turbine power based on long
short-term memory network and Gaussian Mixture Model, Appl. Energy, 241, 229–244, https://doi.org/10.1016/j.apenergy.2019.03.044, 2019. a
Zhang, X., Low, Y. M., and Koh, C. G.: Maximum entropy distribution with
fractional moments for reliability analysis, Struct. Safe., 83, 101904,
https://doi.org/10.1016/j.strusafe.2019.101904, 2020. a
Zhang, Y., Li, M., Wang, S., Dai, S., Luo, L., Zhu, E., Xu, H., Zhu, X., Yao,
C., and Zhou, H.: Gaussian mixture model clustering with incomplete data, ACM
T. Multimed. Comput. Commun. Appl., 17, 1–14, 2021. a
Short summary
Joint probability distribution of 10 min mean wind speed and the standard deviation is proposed using the Gaussian mixture model and has been shown to agree well with 15 years of measurements. The environmental contour with a 50-year return period (extreme turbulence) is estimated. The results from the model could be taken as inputs for structural reliability analysis and uncertainty quantification of wind turbine design loads.
Joint probability distribution of 10 min mean wind speed and the standard deviation is proposed...
Altmetrics
Final-revised paper
Preprint