Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2497-2022
© 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-7-2497-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2022
Research article |
| 20 Dec 2022
| 20 Dec 2022
Lifetime prediction of turbine blades using global precipitation products from satellites
Department of Wind and Energy Systems, Technical University of
Denmark, Roskilde, 4000, Denmark
Haichen Zuo
Department of Wind and Energy Systems, Technical University of
Denmark, Roskilde, 4000, Denmark
Ásta Hannesdóttir
Department of Wind and Energy Systems, Technical University of
Denmark, Roskilde, 4000, Denmark
Abdalmenem Owda
Department of Wind and Energy Systems, Technical University of
Denmark, Roskilde, 4000, Denmark
Charlotte Hasager
Department of Wind and Energy Systems, Technical University of
Denmark, Roskilde, 4000, Denmark
Related authors
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
Short summary
Short summary
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.
Tobias Ahsbahs, Galen Maclaurin, Caroline Draxl, Christopher R. Jackson, Frank Monaldo, and Merete Badger
Wind Energ. Sci., 5, 1191–1210, https://doi.org/10.5194/wes-5-1191-2020, https://doi.org/10.5194/wes-5-1191-2020, 2020
Short summary
Short summary
Before constructing wind farms we need to know how much energy they will produce. This requires knowledge of long-term wind conditions from either measurements or models. At the US East Coast there are few wind measurements and little experience with offshore wind farms. Therefore, we created a satellite-based high-resolution wind resource map to quantify spatial variations in the wind conditions over potential sites for wind farms and found larger variation than modelling suggested.
Charlotte B. Hasager, Andrea N. Hahmann, Tobias Ahsbahs, Ioanna Karagali, Tija Sile, Merete Badger, and Jakob Mann
Wind Energ. Sci., 5, 375–390, https://doi.org/10.5194/wes-5-375-2020, https://doi.org/10.5194/wes-5-375-2020, 2020
Short summary
Short summary
Europe's offshore wind resource mapping is part of the New European Wind Atlas (NEWA) international consortium effort. This study presents the results of analysis of synthetic aperture radar (SAR) ocean wind maps based on Envisat and Sentinel-1 with a brief description of the wind retrieval process and Advanced Scatterometer (ASCAT) ocean wind maps. Furthermore, the Weather Research and Forecasting (WRF) offshore wind atlas of NEWA is presented.
Tobias Ahsbahs, Merete Badger, Patrick Volker, Kurt S. Hansen, and Charlotte B. Hasager
Wind Energ. Sci., 3, 573–588, https://doi.org/10.5194/wes-3-573-2018, https://doi.org/10.5194/wes-3-573-2018, 2018
Short summary
Short summary
Satellites offer wind measurements offshore and can resolve the wind speed on scales of up to 500 m. To date, this data is not routinely used in the industry for planning wind farms. We show that this data can be used to predict local differences in the mean wind speed around the Anholt offshore wind farm. With satellite data, site-specific wind measurements can be introduced early in the planning phase of an offshore wind farm and help decision makers.
Hugo Rubio, Daniel Hatfield, Charlotte Bay Hasager, Martin Kühn, and Julia Gottschall
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-11, https://doi.org/10.5194/amt-2024-11, 2024
Revised manuscript under review for AMT
Short summary
Short summary
Unlocking offshore wind farms’ potential demands a precise understanding of available wind resources. Yet, limited in situ data in marine environments call for innovative solutions. This study delves into the world of satellite remote sensing and numerical models, exploring their capabilities and challenges in characterizing offshore wind dynamics. This investigation evaluates these tools against measurements from a floating ship-based lidar, collected through a novel campaign in the Baltic Sea.
Jens Visbech, Tuhfe Göçmen, Özge Sinem Özçakmak, Alexander Meyer Forsting, Ásta Hannesdóttir, and Pierre-Elouan Réthoré
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-128, https://doi.org/10.5194/wes-2023-128, 2023
Revised manuscript accepted for WES
Short summary
Short summary
Leading edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This study couples LEE prediction, aerodynamic loss modeling, and wind farm flow modeling to show that LEE's effects on wake dynamics can affect overall energy production. Without preventive initiatives, the effects of LEE increase over time, resulting in significant annual energy production (AEP) loss.
Haichen Zuo and Charlotte Bay Hasager
Atmos. Meas. Tech., 16, 3901–3913, https://doi.org/10.5194/amt-16-3901-2023, https://doi.org/10.5194/amt-16-3901-2023, 2023
Short summary
Short summary
Aeolus is a satellite equipped with a Doppler wind lidar to detect global wind profiles. This study evaluates the impact of Aeolus winds on surface wind forecasts over tropical oceans and high-latitude regions based on the ECMWF observing system experiments. We find that Aeolus can slightly improve surface wind forecasts for the region > 60° N, especially from day 5 onwards. For other study regions, the impact of Aeolus is nearly neutral or limited, which requires further investigation.
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.
Ásta Hannesdóttir, David R. Verelst, and Albert M. Urbán
Wind Energ. Sci., 8, 231–245, https://doi.org/10.5194/wes-8-231-2023, https://doi.org/10.5194/wes-8-231-2023, 2023
Short summary
Short summary
In this work we use observations of large coherent fluctuations to define a probabilistic gust model. The gust model provides the joint description of the gust rise time, amplitude, and direction change. We perform load simulations with a coherent gust according to the wind turbine safety standard and with the probabilistic gust model. A comparison of the simulated loads shows that the loads from the probabilistic gust model can be significantly higher due to variability in the gust parameters.
Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen
Wind Energ. Sci., 8, 173–191, https://doi.org/10.5194/wes-8-173-2023, https://doi.org/10.5194/wes-8-173-2023, 2023
Short summary
Short summary
This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
Xiaoli Guo Larsén, Marc Imberger, Ásta Hannesdóttir, and Andrea N. Hahmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-102, https://doi.org/10.5194/wes-2022-102, 2023
Revised manuscript not accepted
Short summary
Short summary
We study how climate change will impact extreme winds and choice of turbine class. We use data from 18 CMIP6 members from a historic and a future period to access the change in the extreme winds. The analysis shows an overall increase in the extreme winds in the North Sea and the southern Baltic Sea, but a decrease over the Scandinavian Peninsula and most of the Baltic Sea. The analysis is inconclusive to whether higher or lower classes of turbines will be installed in the future.
Haichen Zuo, Charlotte Bay Hasager, Ioanna Karagali, Ad Stoffelen, Gert-Jan Marseille, and Jos de Kloe
Atmos. Meas. Tech., 15, 4107–4124, https://doi.org/10.5194/amt-15-4107-2022, https://doi.org/10.5194/amt-15-4107-2022, 2022
Short summary
Short summary
The Aeolus satellite was launched in 2018 for global wind profile measurement. After successful operation, the error characteristics of Aeolus wind products have not yet been studied over Australia. To complement earlier validation studies, we evaluated the Aeolus Level-2B11 wind product over Australia with ground-based wind profiling radar measurements and numerical weather prediction model equivalents. The results show that the Aeolus can detect winds with sufficient accuracy over Australia.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Tobias Ahsbahs, Galen Maclaurin, Caroline Draxl, Christopher R. Jackson, Frank Monaldo, and Merete Badger
Wind Energ. Sci., 5, 1191–1210, https://doi.org/10.5194/wes-5-1191-2020, https://doi.org/10.5194/wes-5-1191-2020, 2020
Short summary
Short summary
Before constructing wind farms we need to know how much energy they will produce. This requires knowledge of long-term wind conditions from either measurements or models. At the US East Coast there are few wind measurements and little experience with offshore wind farms. Therefore, we created a satellite-based high-resolution wind resource map to quantify spatial variations in the wind conditions over potential sites for wind farms and found larger variation than modelling suggested.
Anna-Maria Tilg, Charlotte Bay Hasager, Hans-Jürgen Kirtzel, and Poul Hummelshøj
Wind Energ. Sci., 5, 977–981, https://doi.org/10.5194/wes-5-977-2020, https://doi.org/10.5194/wes-5-977-2020, 2020
Short summary
Short summary
Recently, there has been an increased awareness of leading-edge erosion of wind turbine blades. An option to mitigate the erosion at the leading edges is the deceleration of the wind turbine blades during severe precipitation events. This work shows that a vertically pointing radar can be used to nowcast precipitation events with the required spatial and temporal resolution. Furthermore, nowcasting allows a reduction in the rotational speed prior to the impact of precipitation on the blades.
Charlotte B. Hasager, Andrea N. Hahmann, Tobias Ahsbahs, Ioanna Karagali, Tija Sile, Merete Badger, and Jakob Mann
Wind Energ. Sci., 5, 375–390, https://doi.org/10.5194/wes-5-375-2020, https://doi.org/10.5194/wes-5-375-2020, 2020
Short summary
Short summary
Europe's offshore wind resource mapping is part of the New European Wind Atlas (NEWA) international consortium effort. This study presents the results of analysis of synthetic aperture radar (SAR) ocean wind maps based on Envisat and Sentinel-1 with a brief description of the wind retrieval process and Advanced Scatterometer (ASCAT) ocean wind maps. Furthermore, the Weather Research and Forecasting (WRF) offshore wind atlas of NEWA is presented.
Ásta Hannesdóttir and Mark Kelly
Wind Energ. Sci., 4, 385–396, https://doi.org/10.5194/wes-4-385-2019, https://doi.org/10.5194/wes-4-385-2019, 2019
Short summary
Short summary
The wind turbine safety standard includes a coherent gust model with a wind speed increase and direction change of 10 s. With the increasing rotor size of modern wind turbines this model is criticized for being uniform across these large rotors. In this study we investigate measurements of coherent gusts with a ramp-like increase in wind speed. We define a new method for ramp detection and characterization and compare it with the coherent gust model from the wind turbine safety standard.
Ásta Hannesdóttir, Mark Kelly, and Nikolay Dimitrov
Wind Energ. Sci., 4, 325–342, https://doi.org/10.5194/wes-4-325-2019, https://doi.org/10.5194/wes-4-325-2019, 2019
Short summary
Short summary
We investigate large wind speed fluctuations from a 10-year period at the Danish coastal site Høvsøre. The most extreme fluctuations are not turbulent but due to larger-scale weather phenomena. We find how these fluctuations impact wind turbines using simulations. The results are then compared to an extreme turbulence model described in the wind turbine safety standards, and it is found that the loads on the different turbine components are not the same as what the standard describes.
Jakob Ilsted Bech, Charlotte Bay Hasager, and Christian Bak
Wind Energ. Sci., 3, 729–748, https://doi.org/10.5194/wes-3-729-2018, https://doi.org/10.5194/wes-3-729-2018, 2018
Short summary
Short summary
Rain erosion on wind turbine blades is a severe challenge for wind energy today. It causes significant losses in power production, and large sums are spent on inspection and repair.
Blade life can be extended, power production increased and maintenance costs reduced by rotor speed reduction at extreme precipitation events. Combining erosion test results, meteorological data and models of blade performance, we show that a turbine control strategy is a promising new weapon against blade erosion.
Tobias Ahsbahs, Merete Badger, Patrick Volker, Kurt S. Hansen, and Charlotte B. Hasager
Wind Energ. Sci., 3, 573–588, https://doi.org/10.5194/wes-3-573-2018, https://doi.org/10.5194/wes-3-573-2018, 2018
Short summary
Short summary
Satellites offer wind measurements offshore and can resolve the wind speed on scales of up to 500 m. To date, this data is not routinely used in the industry for planning wind farms. We show that this data can be used to predict local differences in the mean wind speed around the Anholt offshore wind farm. With satellite data, site-specific wind measurements can be introduced early in the planning phase of an offshore wind farm and help decision makers.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model
The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects
Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production
An LES Model for Wind Farm-Induced Atmospheric Gravity Wave Effects Inside Conventionally Neutral Boundary Layers
Simulating low-frequency wind fluctuations
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
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
Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, https://doi.org/10.5194/wes-9-1153-2024, 2024
Short summary
Short summary
Tropical cyclone winds are challenging for wind turbines. We analyze a tropical cyclone before landfall in a mesoscale model. The simulated wind speeds and storm structure are sensitive to the boundary parametrization. However, independent of the boundary layer parametrization, the median change in wind speed and wind direction with height is small relative to wind turbine design standards. Strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1123–1152, https://doi.org/10.5194/wes-9-1123-2024, https://doi.org/10.5194/wes-9-1123-2024, 2024
Short summary
Short summary
This paper introduces the multi-scale coupled (MSC) model, an engineering framework aimed at modeling turbine–wake and wind farm–gravity wave interactions, as well as local and global blockage effects. Comparisons against large eddy simulations show that the MSC model offers a valid contribution towards advancing our understanding of the coupled wind farm–atmosphere interaction, helping refining power estimation methodologies for existing and future wind farm sites.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
Short summary
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-171, https://doi.org/10.5194/wes-2023-171, 2024
Revised manuscript under review for WES
Short summary
Short summary
We introduce a novel way to model the impact of Atmospheric Gravity Waves (AGWs) on wind farms using high-fidelity simulations while significantly reducing computational costs. The proposed approach is validated across different atmospheric stability conditions, and implications of neglecting AGWs when predicting wind farm power are assessed. This work advances our understanding of the wind farm interaction with the free atmosphere, ultimately facilitating cost-effective research.
Abdul Haseeb Syed and Jakob Mann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-142, https://doi.org/10.5194/wes-2023-142, 2023
Revised manuscript accepted for WES
Short summary
Short summary
Wind flow consists of swirling patterns of air called eddies, some as big as many kilometers across, while others are as small as just a few meters. This paper introduces a method to simulate these large swirling patterns on a flat grid. Using these simulations we can better figure out how these large eddies affect big wind turbines in terms of loads and forces.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
Short summary
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.
Cited articles
Arulraj, M. and Barros, A. P.: Shallow precipitation detection and classification using multifrequency radar observations and model simulations, J. Atmos. Ocean. Tech., 34, 1963–1983, https://doi.org/10.1175/JTECH-D-17-0060.1, 2017.
Bak, C., Forsting, A. M., and Sørensen, N. N.: The influence of leading
edge roughness, rotor control and wind climate on the loss in energy production, J. Phys.: Conf. Ser., 1618, 052050,
https://doi.org/10.1088/1742-6596/1618/5/052050, 2020.
Bech, J. I., Hasager, C. B., and Bak, C.: Extending the life of wind turbine
blade leading edges by reducing the tip speed during extreme precipitation
events, Wind Energ. Sci., 3, 729–748, https://doi.org/10.5194/wes-3-729-2018, 2018.
Bech, J. I., Johansen, N. F.-J., Madsen, M. B., Hannesdóttir, Á., and Hasager, C. B.: Experimental study on the effect of drop size in rain erosion test and on lifetime prediction of wind turbine blades, Renew. Energy, 197, 776–789, https://doi.org/10.1016/j.renene.2022.06.127, 2022.
Best, A. C.: The size of distribution of raindrops, Q. J. Roy. Meteorol. Soc., 76, 16–36, https://doi.org/10.1002/qj.49707632704, 1950.
Bogerd, L., Overeem, A., Leijnse, H., and Uijlenhoet, R.: A comprehensive
five-year evaluation of IMERG late run precipitation estimates over the
Netherlands, J. Hydrometeorol., 22, 1855-1868, https://doi.org/10.1175/JHM-D-21-0002.1,
2021.
Chen, F. and Li, X.: Evaluation of IMERG and TRMM 3B43 monthly precipitation
products over mainland China, Remote Sens., 8, 472, https://doi.org/10.3390/rs8060472, 2016.
Cui, W., Dong, X., Xi, B., Feng, Z. H. E., and Fan, J.: Can the GPM IMERG
final product accurately represent MCSs' precipitation characteristics over the central and eastern United States?, J. Hydrometeorol., 21, 39–57,
https://doi.org/10.1175/JHM-D-19-0123.1, 2020.
Dezfuli, A. K., Ichoku, C. M., Mohr, K. I., and Huffman, G. J.: Precipitation characteristics in West and East Africa from satellite and in situ observations, J. Hydrometeorol., 18, 1799–1805, https://doi.org/10.1175/JHM-D-17-0068.1, 2017.
Eisenberg, D., Laustsen, S., and Stege, J.: Wind turbine blade coating leading edge rain erosion model: Development and validation, Wind Energy, 21, 942–951, https://doi.org/10.1002/we.2200, 2018.
Freitas, da S. E., Coelho, V. H. R., Xuan, Y., de Melo, D. C. D., Gadelha, A. N., Santos, E. A., de Galvão, C. O., Ramos, F., Geraldo, M., Barbosa, L. M., Huffman, G. J., Petersen, W. A., and Almeida das N., C.: The performance of the IMERG satellite-based product in identifying sub-daily rainfall events and their properties, J. Hydrol., 589, 125128, https://doi.org/10.1016/j.jhydrol.2020.125128, 2020.
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypiński, W. R., Scott, G.,
Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.:
Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine (NREL/TP-5000-75698), National Renewable Energy Laboratory, Golden, CO, USA, https://www.nrel.gov/docs/fy20osti/75698.pdf (last access: 14 December 2022), 2020.
Hasager, C. B., Vejen, F., Bech, J. I., Skrzypiński, W. R., Tilg, A.-M.,
and Nielsen, M.: Assessment of the rain and wind climate with focus on wind
turbine blade leading edge erosion rate and expected lifetime in Danish Seas, Renew. Energy, 149, 91–102, https://doi.org/10.1016/j.renene.2019.12.043, 2020.
Hasager, C. B., Vejen, F., Skrzypinski, W. R., and Tilg, A.-M.: Rain Erosion
Load and Its Effect on Leading edge Lifetime and Potential of Erosion-Safe
Mode at Wind Turbines in the North Sea and Baltic Sea, Energies, 14, 1959, https://doi.org/10.3390/en14071959, 2021.
Herring, R., Dyer, K., Martin, F., and Ward, C.: The increasing importance of leading edge erosion and a review of existing protection solutions, Renew.
Sustain. Energ. Rev., 115, 109382, https://doi.org/10.1016/j.rser.2019.109382, 2019.
Herring, R., Dyer, K., Howkins, P., and Ward, C.: Characterisation of the
offshore precipitation environment to help combat leading edge erosion of wind turbine blades, Wind Energ. Sci., 5, 1399–1409, https://doi.org/10.5194/wes-5-1399-2020, 2020.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The global precipitation
measurement mission, B. Am. Meteorol. Soc., 95, 701–722,
https://doi.org/10.1175/BAMS-D-13-00164.1, 2014.
Hsu, S. A., Meindl, E. A., and Gilhousen, D. B.: Determining the power-law
wind-profile exponent under near-neutral stability conditions at sea, J. Appl. Meteorol. Clim., 33, 757-765, https://doi.org/10.1175/1520-0450(1994)033<0757:DTPLWP>2.0.CO;2, 1994.
Huffman, G.: IMERG V06 Quality Index, NASA, USA,
https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_QI.pdf
(last access: 14 December 2022), 2019.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor
precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/JHM560.1, 2007.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J. and Tan, J.:
GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree × 0.1 degree V06, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH/06, 2019.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K. L., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., Tan, J., Wolff, D.
B., and Xie, P.: Integrated Multi-satellite Retrievals for the Global
Precipitation Measurement (GPM) Mission (IMERG), Adv. Global Change Res., 67,
343–353, https://doi.org/10.1007/978-3-030-24568-9_19, 2020a.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., and Tan, J.: Integrated
Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation, NASA,
USA,
https://gpm.nasa.gov/resources/documents/IMERG-V06-Technical-Documentation
(last access: 14 December 2022), 2020b.
Ibrahim, M. E. and Medraj, M.: Water droplet erosion of wind turbine blades:
Mechanics, testing, modeling and future perspectives, Materials, 13, 157,
https://doi.org/10.3390/ma13010157, 2020.
Keegan, M. H., Nash, D. H., and Stack, M. M.: On erosion issues associated
with the leading edge of wind turbine blades, J. Phys. D, 46, 383001, https://doi.org/10.1088/0022-3727/46/38/383001, 2013.
Kidd, C., Becker, A., Huffman, G., Muller, C., Joe, P., Jackson, G., and
Kirschbaum, D.: So, how much of the Earth's surface is covered by rain
gauges?, B. Am. Meteorol. Soc., 98, 69–78, https://doi.org/10.1175/BAMS-D-14-00283.1, 2017.
Klepp, C., Kucera, P. A., Burdanowitz, J., and Protat, A.: OceanRAIN – The
Global Ocean Surface-Reference Dataset for Characterization, Validation and
Evaluation of the Water Cycle, in: Satellite Precipitation Measurement.
Advances in Global Change Research, 69, edited by: Levizzani, V., Kidd, C.,
Kirschbaum, D., Kummerow, C., Nakamura, K., and Turk, F., Springer, Cham,
https://doi.org/10.1007/978-3-030-35798-6_10, 2020.
Law, H. and Koutsos, V.: Leading edge erosion of wind turbines: Effect of solid airborne particles and rain on operational wind farms, Wind Energy, 23, 1955–1965, https://doi.org/10.1002/we.2540, 2020.
Le, M. and Chandrasekar, V.: An algorithm for drop-size distribution retrieval from GPM dual-frequency precipitation radar, IEEE T. Geosci. Remote, 52, 6813630, https://doi.org/10.1109/TGRS.2014.2308475, 2014.
Letson, F., Barthelmie, R. J., and Pryor, S. C.: Radar-derived precipitation
climatology for wind turbine blade leading edge erosion, Wind Energ. Sci., 5, 331–347, https://doi.org/10.5194/wes-5-331-2020, 2020.
Macdonald, H., Infield, D., Nash, D. H., and Stack, M. M.: Mapping hail
meteorological observations for prediction of erosion in wind turbines: UK hail meteorological observations, Wind Energy, 19, 777–784, https://doi.org/10.1002/we.1854, 2016.
Maranan, M., Fink, A. H., Knippertz, P., Amekudzi, L. K., Atiah, W. A., and
Stengel, M.: A process-based validation of GPM IMERG and its sources using a
mesoscale rain gauge network in the west African forest zone, J. Hydrometeorol., 21, 729–749, https://doi.org/10.1175/JHM-D-19-0257.1, 2020.
Met Office: Water, Fact sheet 3 — Water in the atmosphere, Met Office,
Exeter, UK,
https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/library-and-archive/library/publications/factsheets/factsheet_3-water-in-the-atmosphere-v02.pdf
(last access: 14 December 2022), 2012.
Mishnaevsky, L. and Thomsen, K.: Costs of repair of wind turbine blades: Influence of technology 695 aspects, Wind Energy, 23, 2247–2255,
https://doi.org/10.1002/we.2552, 2020.
Mishnaevsky Jr., L., Hasager, C. B., Bak, C., Tilg, A.-M., Bech, J. I., Rad,
S. D., and Fæster, S.: Rain erosion of wind turbine blades: Understanding, prevention and protection, Renew. Energy, 169, 953–969,
https://doi.org/10.1016/j.renene.2021.01.044, 2021.
Prakash, S., Mitra, A. K., AghaKouchak, A., Liu, Z., Norouzi, H., and Pai, D. S.: A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region, J. Hydrol., 556, 865–876,
https://doi.org/10.1016/j.jhydrol.2016.01.029, 2018.
Prieto, R. and Karlsson, T.: A model to estimate the effect of variables causing erosion in wind turbine blades, Wind Energy, 24, 1031–1044,
https://doi.org/10.1002/we.2615, 2021.
Rios Gaona, M. F., Overeem, A., Leijnse, H., and Uijlenhoet, R.: First-year
evaluation of GPM rainfall over the Netherlands: IMERG day 1 final run (V03D), J. Hydrometeorol., 17, 2799–2814, https://doi.org/10.1175/JHM-D-16-0087.1, 2016.
Shaw, W. J., Berg, L. K., Debnath, M., Deskos, G., Draxl, C., Ghate, V. P., Hasager, C. B., Kotamarthi, R., Mirocha, J. D., Muradyan, P., Pringle, W. J., Turner, D. D., and Wilczak, J. M.: Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer, Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, 2022.
Skrzypiński, W. R., Bech, J. I., Hasager, C. B., Tilg, A.-M., Bak, C.,
and Vejen, F.: Optimization of the erosion-safe operation of the IEA Wind 15 MW Reference Wind Turbine, J. Phys.: Conf. Ser., 1618, 052034,
https://doi.org/10.1088/1742-6596/1618/5/052034, 2020.
Tan, J., Petersen, W. A., and Tokay, A.: A novel approach to identify sources of errors in IMERG for GPM ground validation, J. Hydrometeorol., 17, 2477–2491, https://doi.org/10.1175/JHM-D-16-0079.1, 2016.
Tapiador, F. J., Navarro, A., García-Ortega, E., Merino, A., Sánchez, J. L., Marcos, C., and Kummerow, C.: The contribution of rain gauges in the calibration of the IMERG product: Results from the first validation over Spain, J. Hydrometeorol., 21, 161–182, https://doi.org/10.1175/JHM-D-19-0116.1, 2020.
Tilg, A.-M., Skrzypiński, W. R., Hannesdóttir, Á., and Hasager, C. B.: Effect of drop-size parameterization and rain amount on blade-lifetime calculations considering leading edge erosion, Wind Energy, 25, 952–967, https://doi.org/10.1002/we.2710, 2022.
Tokay, A., D'Adderio, L. P., Porcù, F., Wolff, D. B., and Petersen, W.
A.: A field study of footprint-scale variability of raindrop size distribution, J. Hydrometeorol., 18, 3165–3179,
https://doi.org/10.1175/jhm-d-17-0003.1, 2017.
Verma, A. S., Jiang, Z., Caboni, M., Verhoef, H., van der Mijle Meijer, H.,
Castro, S. G. P., and Teuwen, J. J. E.: A probabilistic rainfall model to
estimate the leading-edge lifetime of wind turbine blade coating system, Renew. Energy, 178, 1435–1455, https://doi.org/10.1016/j.renene.2021.06.122, 2021a.
Verma, A. S., Jiang, Z., Ren, Z., Caboni, M., Verhoef, H., van der
Mijle-Meijer, H., Castro, S. G. P., and Teuwen, J. J. E.: A probabilistic
long-term framework for site-specific erosion analysis of wind turbine blades: A case study of 31 Dutch sites, Wind Energy, 24, 1315–1336, https://doi.org/10.1002/we.2634, 2021b.
Visbech, J., Göçmen, T., Hasager, C. B., Shkalov, H., Handberg, M., and Nielsen, K. P.: Introducing a data-driven approach to predict site-specific leading edge erosion, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-55, in review, 2022.
Xiong, W., Tang, G., Wang, T., Ma, Z., and Wan, W.: Evaluation of IMERG and
ERA5 Precipitation-Phase Partitioning on the Global Scale, Water, 14, 1122, https://doi.org/10.3390/w14071122, 2022.
Short summary
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.
When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and...
Altmetrics
Final-revised paper
Preprint