Articles | Volume 7, issue 5
https://doi.org/10.5194/wes-7-1869-2022
© 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-7-1869-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)
Adithya Vemuri
CORRESPONDING AUTHOR
Department of Environmental and Applied Fluid Dynamics, von Karman Institute for Fluid Dynamics, Waterloosesteenweg 72, 1640 Sint-Genesius-Rode, Belgium
Department of Mechanical Engineering, Vrije Universiteit Brussel, Boulevard de la Plaine 2, 1050 Ixelles, Belgium
SIM vzw, Technologiepark 48, 9052 Zwijnaarde, Belgium
Sophia Buckingham
Department of Environmental and Applied Fluid Dynamics, von Karman Institute for Fluid Dynamics, Waterloosesteenweg 72, 1640 Sint-Genesius-Rode, Belgium
Wim Munters
Department of Environmental and Applied Fluid Dynamics, von Karman Institute for Fluid Dynamics, Waterloosesteenweg 72, 1640 Sint-Genesius-Rode, Belgium
Jan Helsen
Department of Mechanical Engineering, Vrije Universiteit Brussel, Boulevard de la Plaine 2, 1050 Ixelles, Belgium
Jeroen van Beeck
Department of Environmental and Applied Fluid Dynamics, von Karman Institute for Fluid Dynamics, Waterloosesteenweg 72, 1640 Sint-Genesius-Rode, Belgium
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Diederik van Binsbergen, Pieter-Jan Daems, Timothy Verstraeten, Amir Nejad, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-98, https://doi.org/10.5194/wes-2023-98, 2023
Revised manuscript under review for WES
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Wind farm yield assessment often relies on analytical wake models. Calibrating these models can be challenging, due to the stochastic nature of wind. We developed a calibration framework that performs a multi-phase optimization on the tuning parameters using time series data. This yields a parameter distribution that more accurately reflects reality than a single value. Results revealed notable variation in resultant parameter values, influenced by nearby wind farms and coastal effects.
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 8, 893–924, https://doi.org/10.5194/wes-8-893-2023, https://doi.org/10.5194/wes-8-893-2023, 2023
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This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction for wind turbines using SCADA data. The focus lies on techniques that respond to requirements of the industry, e.g., robustness, transparency, computational efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e., generator bearing failures, generator fan failures, and generator stator failures.
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
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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.
Florian Hammer, Sarah Barber, Sebastian Remmler, Federico Bernardoni, Kartik Venkatraman, Gustavo A. Díez Sánchez, Alain Schubiger, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Giljarhus
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-114, https://doi.org/10.5194/wes-2022-114, 2023
Preprint under review for WES
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We further enhanced a knowledge base for choosing the most optimal wind resource assessment tool. For this, we compared different simulation tools for the Perdigão site in Portugal, in terms of accuracy and costs. In total five different simulation tools were compared. We found that with a high degree of automatisation and a high experience level of the modeller a cost effective and accurate prediction based on RANS could be achieved. LES simulations are still mainly reserved for academia.
Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, https://doi.org/10.5194/wes-7-387-2022, 2022
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This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps.
Sara Porchetta, Orkun Temel, Domingo Muñoz-Esparza, Joachim Reuder, Jaak Monbaliu, Jeroen van Beeck, and Nicole van Lipzig
Atmos. Chem. Phys., 19, 6681–6700, https://doi.org/10.5194/acp-19-6681-2019, https://doi.org/10.5194/acp-19-6681-2019, 2019
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Two-way feedback occurs between offshore wind and waves. Using an extensive data set of offshore measurements, we show that the wave roughness affecting the wind is dependent on the alignment between the wind and wave directions. Moreover, we propose a new roughness parameterization that takes into account the dependence on alignment. Using this in numerical models will facilitate a better representation of offshore wind, which is relevant to wind energy and and climate modeling.
Wim Munters and Johan Meyers
Wind Energ. Sci., 3, 409–425, https://doi.org/10.5194/wes-3-409-2018, https://doi.org/10.5194/wes-3-409-2018, 2018
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Wake interactions in wind farms result in power losses for downstream turbines. We aim to mitigate these losses through coordinated control of the induced slowdown of the wind by each turbine. We further analyze results from earlier work towards the utilization of such control strategies in practice. Coherent vortex shedding is identified and mimicked by a sinusoidal control. The latter is shown to increase power in downstream turbines and is robust to turbine spacing and turbulence intensity.
Nikolaos Stergiannis, Jeroen van Beeck, and Mark C. Runacres
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2017-6, https://doi.org/10.5194/wes-2017-6, 2017
Revised manuscript not accepted
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The development of large-scale wind energy projects has created the demand for increasingly accurate and efficient models that limit a project's uncertainties and risk. Wake effects are of great importance and are relevant for the optimization of wind farms. In the present paper, different Computational Fluid Dynamics (CFD) simulations are investigated and compared with single wake measurements of a wind turbine in a wind tunnel. Results show that CFD can predict the wake effects downstream.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: a comparison between two-parameter and three-parameter Weibull distributions
Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy
Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers
Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic
Lifetime prediction of turbine blades using global precipitation products from satellites
Evaluation of low-level jets in the southern Baltic Sea: a comparison between ship-based lidar observational data and numerical models
Predicting power ramps from joint distributions of future wind speeds
Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer
Research challenges and needs for the deployment of wind energy in hilly and mountainous regions
Observer-based power forecast of individual and aggregated offshore wind turbines
Mohammad Golam Mostafa Khan and Mohammed Rafiuddin Ahmed
Wind Energ. Sci., 8, 1277–1298, https://doi.org/10.5194/wes-8-1277-2023, https://doi.org/10.5194/wes-8-1277-2023, 2023
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A robust technique for wind resource assessment with a Bayesian approach for estimating Weibull parameters is proposed. Research conducted using seven sites' data in the tropical region from 1° N to 21° S revealed that the three-parameter (3-p) Weibull distribution with a non-zero shift parameter is a better fit for wind data that have a higher percentage of low wind speeds. Wind data with higher wind speeds are a special case of the 3-p distribution. This approach gives accurate results.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
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The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
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The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
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As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
Wind Energ. Sci., 7, 2497–2512, https://doi.org/10.5194/wes-7-2497-2022, https://doi.org/10.5194/wes-7-2497-2022, 2022
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When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
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A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon
Wind Energ. Sci., 7, 2393–2405, https://doi.org/10.5194/wes-7-2393-2022, https://doi.org/10.5194/wes-7-2393-2022, 2022
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The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Andrew Clifton, Sarah Barber, Alexander Stökl, Helmut Frank, and Timo Karlsson
Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, https://doi.org/10.5194/wes-7-2231-2022, 2022
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The transition to low-carbon sources of energy means that wind turbines will need to be built in hilly or mountainous regions or in places affected by icing. These locations are called
complexand are hard to develop. This paper sets out the research and development (R&D) needed to make it easier and cheaper to harness wind energy there. This includes collaborative R&D facilities, improved wind and weather models, frameworks for sharing data, and a clear definition of site complexity.
Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
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Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
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Short summary
The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
The sensitivity of the WRF mesoscale modeling framework in accurately representing and...