Articles | Volume 8, issue 6
https://doi.org/10.5194/wes-8-947-2023
© Author(s) 2023. 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-8-947-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jun 2023
Research article |
| 07 Jun 2023
| 07 Jun 2023
Grand challenges in the digitalisation of wind energy
Andrew Clifton
CORRESPONDING AUTHOR
Stuttgart Wind Energy at the Institute of Aircraft Design, University of Stuttgart, Stuttgart, Germany
now at: enviConnect, TTI GmbH, 70569 Stuttgart, Germany
Sarah Barber
Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland
Andrew Bray
MXV Ventures, Oakland, California, USA
now at: Aurora Energy Research, Oakland, California, USA
Peter Enevoldsen
Centre for Energy Technologies, Aarhus University, Aarhus, Denmark
Jason Fields
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, USA
Anna Maria Sempreviva
Department of Wind Energy, DTU, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Lindy Williams
Computational Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
Julian Quick
The Paul M. Rady Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA
now at: Department of Wind Energy and Energy Systems, DTU, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mike Purdue
NRG Sytems, Hinesburg, VT, USA
Philip Totaro
IntelStor LLC, Houston, TX, USA
Yu Ding
Department of Industrial and Systems Engineering, Texas A & M University, College Station, TX, USA
Related authors
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
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.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
Short summary
Short summary
This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, https://doi.org/10.5194/wes-5-199-2020, 2020
Short summary
Short summary
This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 2, 295–306, https://doi.org/10.5194/wes-2-295-2017, https://doi.org/10.5194/wes-2-295-2017, 2017
Short summary
Short summary
We use upwind and nacelle-based measurements from a wind turbine and investigate the influence of atmospheric stability and turbulence regimes on nacelle transfer functions (NTFs) used to correct nacelle-mounted anemometer measurements. This work shows that correcting nacelle winds using NTFs results in similar energy production estimates to those obtained using upwind tower-based wind speeds. Further, stability and turbulence metrics have been found to have an effect on NTFs below rated speed.
Jennifer F. Newman and Andrew Clifton
Wind Energ. Sci., 2, 77–95, https://doi.org/10.5194/wes-2-77-2017, https://doi.org/10.5194/wes-2-77-2017, 2017
Short summary
Short summary
Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 1, 221–236, https://doi.org/10.5194/wes-1-221-2016, https://doi.org/10.5194/wes-1-221-2016, 2016
Short summary
Short summary
We use turbine nacelle-based measurements and measurements from an upwind tower to calculate wind turbine power curves and predict the production of energy. We explore how different atmospheric parameters impact these power curves and energy production estimates. Results show statistically significant differences between power curves and production estimates calculated with turbulence and stability filters, and we suggest implementing an additional step in analyzing power performance data.
J. K. Lundquist, M. J. Churchfield, S. Lee, and A. Clifton
Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, https://doi.org/10.5194/amt-8-907-2015, 2015
Short summary
Short summary
Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications like wind energy, but their use often relies on assuming homogeneity in the wind. Using numerical simulations of stable flow past a wind turbine, we quantify the error expected because of the inhomogeneity of the flow. Large errors (30%) in winds are found near the wind turbine, but by three rotor diameters downwind, errors in the horizontal components have decreased to 15% of the inflow.
Julian Quick, Edward Hart, Marcus Binder Nilsen, Rasmus Sode Lund, Jaime Liew, Piinshin Huang, Pierre-Elouan Rethore, Jonathan Keller, Wooyong Song, and Yi Guo
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-63, https://doi.org/10.5194/wes-2025-63, 2025
Preprint under review for WES
Short summary
Short summary
Wind turbine main bearings often fail prematurely, creating costly maintenance challenges. This study examined how wake effects – where upstream turbines create disturbed airflow that impacts downstream turbines – affect bearing lifespans. Using computer simulations, we found that wake effects reduce bearing life by 16% on average. The direction of wake impact matters significantly due to interactions between wind forces and gravity, informing better wind turbine and farm farm design strategies.
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das
Wind Energ. Sci., 10, 559–578, https://doi.org/10.5194/wes-10-559-2025, https://doi.org/10.5194/wes-10-559-2025, 2025
Short summary
Short summary
This research develops a new method for assessing hybrid power plant (HPP) profitability, combining wind and battery systems. It addresses the need for an efficient, accurate, and comprehensive operational model by approximating a state-of-the-art energy management system (EMS) for spot market power bidding using machine learning. The approach significantly reduces computational demands while maintaining high accuracy. It thus opens new possibilities in terms of optimizing the design of HPPs.
Philip Imanuel Franz, Imad Abdallah, Gregory Duthé, Julien Deparday, Ali Jafarabadi, Alexander Popp, Sarah Barber, and Eleni Chatzi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-26, https://doi.org/10.5194/wes-2025-26, 2025
Revised manuscript under review for WES
Short summary
Short summary
New designs of large wind turbine blades have become increasingly flexible, and thus need cost-efficient monitoring solutions. Hence, we investigate if aerodynamic pressure measurements from a low-cost sensing system can be used to detect structural damage. Our research is based on a wind tunnel study, emulating a simplified wind turbine blade under various conditions. We show that using a convolutional neural network-based method, structural damage can indeed be detected and its severity rated.
Thuy-Hai Nguyen, Julian Quick, Pierre-Elouan Réthoré, Jean-François Toubeau, Emmanuel De Jaeger, and François Vallée
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-131, https://doi.org/10.5194/wes-2024-131, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Current offshore wind farms have been designed to maximize their production of electricity at all times, and not to keep some reserve power in case of unexpected events on the grid. We present a new formulation for designing wind farms to maximize revenues from both energy and reserve markets. It is applied on a real-life wind farm. We show that profits are expected to increase in a significant way for wind farms designed and operated for reserve, with less energy supplied.
Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber
Wind Energ. Sci., 9, 883–917, https://doi.org/10.5194/wes-9-883-2024, https://doi.org/10.5194/wes-9-883-2024, 2024
Short summary
Short summary
This paper delves into the crucial task of transforming raw data into actionable knowledge which can be used by advanced artificial intelligence systems – a challenge that spans various domains, industries, and scientific fields amid their digital transformation journey. This article underscores the significance of cross-industry collaboration and learning, drawing insights from sectors leading in digitalisation, and provides strategic guidance for further development in this area.
Javier Criado Risco, Rafael Valotta Rodrigues, Mikkel Friis-Møller, Julian Quick, Mads Mølgaard Pedersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 585–600, https://doi.org/10.5194/wes-9-585-2024, https://doi.org/10.5194/wes-9-585-2024, 2024
Short summary
Short summary
Wind energy developers frequently have to face some spatial restrictions at the time of designing a new wind farm due to different reasons, such as the existence of protected natural areas around the wind farm location, fishing routes, and the presence of buildings. Wind farm design has to account for these restricted areas, but sometimes this is not straightforward to achieve. We have developed a methodology that allows for different inclusion and exclusion areas in the optimization framework.
Rafael Valotta Rodrigues, Mads Mølgaard Pedersen, Jens Peter Schøler, Julian Quick, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 321–341, https://doi.org/10.5194/wes-9-321-2024, https://doi.org/10.5194/wes-9-321-2024, 2024
Short summary
Short summary
The use of wind energy has been growing over the last few decades, and further increase is predicted. As the wind energy industry is starting to consider larger wind farms, the existing numerical methods for analysis of small and medium wind farms need to be improved. In this article, we have explored different strategies to tackle the problem in a feasible and timely way. The final product is a set of recommendations when carrying out trade-off analysis on large wind farms.
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.
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 withdrawn
Short summary
Short summary
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.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
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.
Julian Quick, Ryan N. King, Garrett Barter, and Peter E. Hamlington
Wind Energ. Sci., 7, 1941–1955, https://doi.org/10.5194/wes-7-1941-2022, https://doi.org/10.5194/wes-7-1941-2022, 2022
Short summary
Short summary
Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of alignment with the incoming wind, thereby steering wakes away from downstream turbines. Trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present an optimization approach for efficiently exploring the trade-offs between power and loading during wake steering.
Gianluca De Fezza and Sarah Barber
Wind Energ. Sci., 7, 1627–1640, https://doi.org/10.5194/wes-7-1627-2022, https://doi.org/10.5194/wes-7-1627-2022, 2022
Short summary
Short summary
As part of a master's thesis, this study analysed the aerodynamic performance of a multi-element airfoil using numerical flow simulations. The results show that these types of airfoil are very suitable for an upcoming wind energy generation concept. The parametric study of the wing led to a significant improvement of up to 46.6 % compared to the baseline design. The increased power output of the energy generation concept contributes substantially to today's energy transition.
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.
Sarah Barber, Julien Deparday, Yuriy Marykovskiy, Eleni Chatzi, Imad Abdallah, Gregory Duthé, Michele Magno, Tommaso Polonelli, Raphael Fischer, and Hanna Müller
Wind Energ. Sci., 7, 1383–1398, https://doi.org/10.5194/wes-7-1383-2022, https://doi.org/10.5194/wes-7-1383-2022, 2022
Short summary
Short summary
Aerodynamic and acoustic field measurements on operating large-scale wind turbines are key for the further reduction in the costs of wind energy. In this work, a novel cost-effective MEMS (micro-electromechanical systems)-based aerodynamic and acoustic wireless measurement system that is thin, non-intrusive, easy to install, low power and self-sustaining is designed and tested.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
Short summary
Short summary
This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Joseph C. Y. Lee and M. Jason Fields
Wind Energ. Sci., 6, 311–365, https://doi.org/10.5194/wes-6-311-2021, https://doi.org/10.5194/wes-6-311-2021, 2021
Short summary
Short summary
This review paper evaluates the energy prediction bias in the wind resource assessment process, and the overprediction bias is decreasing over time. We examine the estimated and observed losses and uncertainties in energy production from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 standard. The considerable uncertainties call for further improvements in the prediction methodologies and more observations for validation.
Alain Schubiger, Sarah Barber, and Henrik Nordborg
Wind Energ. Sci., 5, 1507–1519, https://doi.org/10.5194/wes-5-1507-2020, https://doi.org/10.5194/wes-5-1507-2020, 2020
Short summary
Short summary
A large-eddy simulation using the lattice Boltzmann method (LBM) Palabos framework was implemented to calculate the wind field over the complex terrain of Bolund Hill. The results were compared to Reynolds-averaged Navier–Stokes and detached-eddy simulation (DES) using Ansys Fluent and field measurements. A comparison of the three methods' computational costs has shown that the LBM, even though not yet fully optimised, can perform 5 times faster than DES and lead to reasonably accurate results.
Julian Quick, Jennifer King, Ryan N. King, Peter E. Hamlington, and Katherine Dykes
Wind Energ. Sci., 5, 413–426, https://doi.org/10.5194/wes-5-413-2020, https://doi.org/10.5194/wes-5-413-2020, 2020
Short summary
Short summary
We investigate the trade-offs in optimization of wake steering strategies, where upstream turbines are positioned to deflect wakes away from downstream turbines, with a probabilistic perspective. We identify inputs that are sensitive to uncertainty and demonstrate a realistic optimization under uncertainty for a wind power plant control strategy. Designing explicitly around uncertainty yielded control strategies that were generally less aggressive and more robust to the uncertain input.
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, https://doi.org/10.5194/wes-5-199-2020, 2020
Short summary
Short summary
This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
Sarah Barber, Alain Schubiger, Natalie Wagenbrenner, Nicolas Fatras, and Henrik Nordborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-95, https://doi.org/10.5194/wes-2019-95, 2020
Publication in WES not foreseen
Short summary
Short summary
A new method for helping wind modellers choose the most cost-effective model for a given project is developed by applying six different Computational Fluid Dynamics tools to simulate the Bolund Hill experiment and studying appropriate comparison metrics in detail. The results show that this new method is successful, and that it is generally possible to apply it in order to choose the most appropriate model for a given project in advance.
Sarah Barber, Simon Boller, and Henrik Nordborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-97, https://doi.org/10.5194/wes-2019-97, 2019
Revised manuscript not accepted
Short summary
Short summary
The growing worldwide level of renewable power generation requires innovative solutions to maintain grid reliability and stability. In this work, twelve sites in Switzerland are chosen for a 100 % renewable energy microgrid feasibility study. For all of these sites, a combination of wind and PV performs consistently better than wind only and PV only. Five of the sites are found to be potentially economically viable, if investors would be prepared to make extra investments of 0.05–0.2 $/kWh.
Mike Optis, Jordan Perr-Sauer, Caleb Philips, Anna E. Craig, Joseph C. Y. Lee, Travis Kemper, Shuangwen Sheng, Eric Simley, Lindy Williams, Monte Lunacek, John Meissner, and M. Jason Fields
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-12, https://doi.org/10.5194/wes-2019-12, 2019
Preprint withdrawn
Short summary
Short summary
As global wind capacity continues to grow, the need for accurate operational analyses of a rapidly growing fleet of wind power plants has increased in proportion. To address this need, the National Renewable Energy Laboratory has released OpenOA, an open-source codebase for operational analysis of wind farms. It is envisioned that OpenOA will evolve into a widely used codebase supported by a large group of global wind energy experts. This paper provides a summary of OpenOA.
Joseph C. Y. Lee, M. Jason Fields, and Julie K. Lundquist
Wind Energ. Sci., 3, 845–868, https://doi.org/10.5194/wes-3-845-2018, https://doi.org/10.5194/wes-3-845-2018, 2018
Short summary
Short summary
To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using 37 years of wind and energy data. We conclude that the robust coefficient of variation can effectively assess and correlate wind-speed and energy-production variabilities. We derive adequate results via monthly mean data, whereas uncertainty arises in interannual variability calculations. We find that reliable estimates of wind-speed variability require 10 ± 3 years of monthly mean wind data.
Rogier Floors, Peter Enevoldsen, Neil Davis, Johan Arnqvist, and Ebba Dellwik
Wind Energ. Sci., 3, 353–370, https://doi.org/10.5194/wes-3-353-2018, https://doi.org/10.5194/wes-3-353-2018, 2018
Short summary
Short summary
Applying erroneous boundary conditions (surface roughness) for wind flow modelling can have a large impact on the estimated performance of wind turbines, particularly in forested areas. Traditionally the estimation of the surface roughness is based on a subjective process that requires assigning a value to each land use class in the vicinity of the wind farm. Here we propose a new method which converts lidar scans from a plane into maps that can be used for wind flow modelling.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 2, 295–306, https://doi.org/10.5194/wes-2-295-2017, https://doi.org/10.5194/wes-2-295-2017, 2017
Short summary
Short summary
We use upwind and nacelle-based measurements from a wind turbine and investigate the influence of atmospheric stability and turbulence regimes on nacelle transfer functions (NTFs) used to correct nacelle-mounted anemometer measurements. This work shows that correcting nacelle winds using NTFs results in similar energy production estimates to those obtained using upwind tower-based wind speeds. Further, stability and turbulence metrics have been found to have an effect on NTFs below rated speed.
Bjarke T. Olsen, Andrea N. Hahmann, Anna Maria Sempreviva, Jake Badger, and Hans E. Jørgensen
Wind Energ. Sci., 2, 211–228, https://doi.org/10.5194/wes-2-211-2017, https://doi.org/10.5194/wes-2-211-2017, 2017
Short summary
Short summary
Understanding uncertainties in wind resource assessment associated with the use of the output from numerical weather prediction (NWP) models is important for wind energy applications. A better understanding of the sources of error reduces risk and lowers costs. Here, an intercomparison of the output from 25 NWP models is presented. The study shows that model errors are larger and agreement between models smaller at inland sites and near the surface.
Jennifer F. Newman and Andrew Clifton
Wind Energ. Sci., 2, 77–95, https://doi.org/10.5194/wes-2-77-2017, https://doi.org/10.5194/wes-2-77-2017, 2017
Short summary
Short summary
Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.
Clara M. St. Martin, Julie K. Lundquist, Andrew Clifton, Gregory S. Poulos, and Scott J. Schreck
Wind Energ. Sci., 1, 221–236, https://doi.org/10.5194/wes-1-221-2016, https://doi.org/10.5194/wes-1-221-2016, 2016
Short summary
Short summary
We use turbine nacelle-based measurements and measurements from an upwind tower to calculate wind turbine power curves and predict the production of energy. We explore how different atmospheric parameters impact these power curves and energy production estimates. Results show statistically significant differences between power curves and production estimates calculated with turbulence and stability filters, and we suggest implementing an additional step in analyzing power performance data.
L. Tiriolo, R. C. Torcasio, S. Montesanti, A. M. Sempreviva, C. R. Calidonna, C. Transerici, and S. Federico
Adv. Sci. Res., 12, 37–44, https://doi.org/10.5194/asr-12-37-2015, https://doi.org/10.5194/asr-12-37-2015, 2015
Short summary
Short summary
We show a study of the prediction of power production of a wind farm located in Central Italy using RAMS model for wind speed forecast.
J. K. Lundquist, M. J. Churchfield, S. Lee, and A. Clifton
Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, https://doi.org/10.5194/amt-8-907-2015, 2015
Short summary
Short summary
Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications like wind energy, but their use often relies on assuming homogeneity in the wind. Using numerical simulations of stable flow past a wind turbine, we quantify the error expected because of the inhomogeneity of the flow. Large errors (30%) in winds are found near the wind turbine, but by three rotor diameters downwind, errors in the horizontal components have decreased to 15% of the inflow.
I. M. Mazzitelli, M. Cassol, M. M. Miglietta, U. Rizza, A. M. Sempreviva, and A. S. Lanotte
Nonlin. Processes Geophys., 21, 489–501, https://doi.org/10.5194/npg-21-489-2014, https://doi.org/10.5194/npg-21-489-2014, 2014
Related subject area
Thematic area: Materials and operation | Topic: Operation and maintenance, condition monitoring, reliability
A machine-learning-based approach for active monitoring of blade pitch misalignment in wind turbines
On the modeling errors of digital twins for load monitoring and fatigue assessment in wind turbine drivetrains
Leveraging Signal Processing and Machine Learning for Automated Fault Detection in Wind Turbine Drivetrains
Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements
Unsupervised anomaly detection of permanent-magnet offshore wind generators through electrical and electromagnetic measurements
Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences
Operation and maintenance cost comparison between 15 MW direct-drive and medium-speed offshore wind turbines
Sensitivity of fatigue reliability in wind turbines: effects of design turbulence and the Wöhler exponent
Active trailing edge flap system fault detection via machine learning
Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms
Assessing the rotor blade deformation and tower–blade tip clearance of a 3.4 MW wind turbine with terrestrial laser scanning
Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations
Population Based Structural Health Monitoring: Homogeneous Offshore Wind Model Development
Wind turbine main-bearing lubrication – Part 2: Simulation-based results for a double-row spherical roller main bearing in a 1.5 MW wind turbine
Reduction of wind-turbine-generated seismic noise with structural measures
Very low frequency IEPE accelerometer calibration and application to a wind energy structure
Sabrina Milani, Jessica Leoni, Stefano Cacciola, Alessandro Croce, and Mara Tanelli
Wind Energ. Sci., 10, 497–510, https://doi.org/10.5194/wes-10-497-2025, https://doi.org/10.5194/wes-10-497-2025, 2025
Short summary
Short summary
In this paper, we propose a novel machine-learning framework for pitch misalignment detection in wind turbines. Using a minimal set of standard sensors, our method detects misalignments as small as 0.1° and localizes the affected blades. It combines signal processing with a hierarchical classification structure and linear regression for precise severity quantification. Evaluation results validate the approach, showing notable accuracy in misalignment classification, regression, and localization.
Felix C. Mehlan and Amir R. Nejad
Wind Energ. Sci., 10, 417–433, https://doi.org/10.5194/wes-10-417-2025, https://doi.org/10.5194/wes-10-417-2025, 2025
Short summary
Short summary
A digital twin is a virtual representation that mirrors the wind turbine's real behavior through simulation models and sensor measurements and can assist in making key decisions such as planning the replacement of parts. These models and measurements are, of course, not perfect and only give an incomplete picture of the real behavior. This study investigates how large the uncertainty of such models and measurements is and to what extent it affects the decision-making process.
Faras Jamil, Cédric Peeters, Timothy Verstraeten, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-114, https://doi.org/10.5194/wes-2024-114, 2024
Revised manuscript accepted for WES
Short summary
Short summary
A hybrid fault detection method is proposed, which combines physical domain knowledge with machine learning models to automatically detect mechanical faults in wind turbine drivetrain components. It offers detailed insights for experts while giving operators a high-level overview of the machine's health to assist in planning effective maintenance strategies. It was validated on multiple years of wind farm data and the potential faults were accurately predicted, which was confirmed by experts.
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024, https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Short summary
This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
Ali Dibaj, Mostafa Valavi, and Amir R. Nejad
Wind Energ. Sci., 9, 2063–2086, https://doi.org/10.5194/wes-9-2063-2024, https://doi.org/10.5194/wes-9-2063-2024, 2024
Short summary
Short summary
This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore methods of early fault detection. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large megawatt-scale offshore wind generators.
Tahir H. Malik and Christian Bak
Wind Energ. Sci., 9, 2017–2037, https://doi.org/10.5194/wes-9-2017-2024, https://doi.org/10.5194/wes-9-2017-2024, 2024
Short summary
Short summary
We explore the effect of blade modifications on offshore wind turbines' performance through a detailed analysis of 12 turbines over 12 years. Introducing the turbine performance integral method, which utilises time-series decomposition that combines various data sources, we uncover how blade wear, repairs and software updates impact efficiency. The findings offer valuable insights into improving wind turbine operations, contributing to the enhancement of renewable energy technologies.
Orla Donnelly, Fraser Anderson, and James Carroll
Wind Energ. Sci., 9, 1345–1362, https://doi.org/10.5194/wes-9-1345-2024, https://doi.org/10.5194/wes-9-1345-2024, 2024
Short summary
Short summary
We collate the latest reliability data in operations and maintenance (O&M) for offshore wind turbines, specifically large turbines of 15 MW. We use these data to model O&M of an offshore wind farm at three different sites. We compare two industry-dominant drivetrain configurations in terms of O&M cost for 15 MW turbines and determine if previous results for smaller turbines still hold true. Comparisons between drivetrains are topical within industry, and we produce cost comparisons for them.
Shadan Mozafari, Paul Veers, Jennifer Rinker, and Katherine Dykes
Wind Energ. Sci., 9, 799–820, https://doi.org/10.5194/wes-9-799-2024, https://doi.org/10.5194/wes-9-799-2024, 2024
Short summary
Short summary
Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how to model the turbulence in normal wind conditions in the design phase. To address such debates, we study the fatigue load distribution and reliability following different models of the International Electrotechnical Commission 61400-1 standard. The results show the lesser importance of load uncertainty due to turbulence distribution compared to the uncertainty of material resistance and Miner’s rule.
Andrea Gamberini and Imad Abdallah
Wind Energ. Sci., 9, 181–201, https://doi.org/10.5194/wes-9-181-2024, https://doi.org/10.5194/wes-9-181-2024, 2024
Short summary
Short summary
Active trailing edge flaps can potentially reduce wind turbine (WT) loads. To monitor their performance, we present two methods based on machine learning that identify flap health states, including degraded performance, in normal power production and idling condition. Both methods rely only on sensors commonly available on WTs. One approach properly detects all the flap states if a fault occurs on only one blade. The other approach can identify two specific flap states in all fault scenarios.
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
Short summary
Short summary
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.
Paula Helming, Alex Intemann, Klaus-Peter Webersinke, Axel von Freyberg, Michael Sorg, and Andreas Fischer
Wind Energ. Sci., 8, 421–431, https://doi.org/10.5194/wes-8-421-2023, https://doi.org/10.5194/wes-8-421-2023, 2023
Short summary
Short summary
Using renewable energy such as wind energy is vital. To optimize the energy yield from wind turbines, they have increased in size, leading to large blade deformations. This paper measures these deformations for different wind loads and the distance between the blade and the tower from 170 m away from the wind turbine. The paper proves that the blade deformation increases in the wind direction with increasing wind speed, while the distance between the blade and the tower decreases.
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.
Innes Murdo Black, Moritz Werther Häckell, and Athanasios Kolios
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-93, https://doi.org/10.5194/wes-2022-93, 2022
Revised manuscript accepted for WES
Short summary
Short summary
Population based structural health monitoring is a low-cost monitoring campaign. The cost reduction from this type of digital enabled asset management tool is manifested by sharing information, in this case a wind farm foundation, within the population. By sharing the information in the wind farm this reduces the amount of sensors and physical model updating, reducing the cost of the monitoring campaign.
Edward Hart, Elisha de Mello, and Rob Dwyer-Joyce
Wind Energ. Sci., 7, 1533–1550, https://doi.org/10.5194/wes-7-1533-2022, https://doi.org/10.5194/wes-7-1533-2022, 2022
Short summary
Short summary
This paper is the second in a two-part study on lubrication in wind turbine main bearings. Investigations are conducted concerning lubrication in the double-row spherical roller main bearing of a 1.5 MW wind turbine. This includes effects relating to temperature, starvation, grease-thickener interactions and possible non-steady EHL effects. Results predict that the modelled main bearing would be expected to operate under mixed lubrication conditions for a non-negligible proportion of its life.
Rafael Abreu, Daniel Peter, and Christine Thomas
Wind Energ. Sci., 7, 1227–1239, https://doi.org/10.5194/wes-7-1227-2022, https://doi.org/10.5194/wes-7-1227-2022, 2022
Short summary
Short summary
In order to find consensus between wind energy producers and seismologists, we study the possibility of reducing wind turbine noise recorded at seismological stations. We find that drilling half-circular holes in front of the wind turbines helps to reduce the seismic noise. We also study the influence of topographic effects on seismic noise reduction.
Clemens Jonscher, Benedikt Hofmeister, Tanja Grießmann, and Raimund Rolfes
Wind Energ. Sci., 7, 1053–1067, https://doi.org/10.5194/wes-7-1053-2022, https://doi.org/10.5194/wes-7-1053-2022, 2022
Short summary
Short summary
This work presents a method to use low-noise IEPE sensors in the low-frequency range down to 0.05 Hz. In order to achieve phase and amplitude accuracy with this type of sensor in the low-frequency range, a new calibration procedure for this frequency range was developed. The calibration enables the use of the low-noise IEPE sensors for large structures, such as wind turbines. The calibrated sensors can be used for wind turbine monitoring, such as fatigue monitoring.
Cited articles
Acumen: Telecom Equipment Market Size, Share, Analysis Report By Component
(Hardware, Software), By Infrastructure (Wired, Wireless), By Technology (2G
and 3G, 4G LTE, 5G), By End-user (BFSI, Retail, IT and Telecommunication,
Media and Entertainment, Healthcare, Military and Defense, Consumer
Electronics, Others), And Region Forecast, 2022–2030,
https://www.acumenresearchandconsulting.com/telecom-equipment-market,
last access: 1 February 2023. a
Ahmed, M. A. and Kim, Y.-C.: Communication network architectures for smart-wind power farms, Energies, 7, 3900–3921, 2014. a
Anderson, M. and Mortensen, N.: Comparative Resource and Energy Yield
Assessment Procedures (CREYAP) Pt. II, AWEA Wind Resource & Project
Energy Assessment Seminar, 10–11 December 2013, Las Vegas, NV, USA, https://orbit.dtu.dk/en/publications/comparative-resource-and-energy-yield-assessment-procedures (last access: 1 February 2023), 2013. a
Bach-Andersen, M., Winther, O., and Rømer-Odgaard, B.: Scalable systems for early fault detection in wind turbines: a data driven approach, in: Annual Conference of the European Wind Energy Association, 17–20 November 2015, Paris, France,
https://www.ewea.org/annual2015/conference/submit-an-abstract/pdf/6401120788396.pdf (last access: 1 February 2023), 2015. a
Benjamin, M., Gagnon, P., Rostamzadeh, N., Pal, C., Bengio, Y., and Shee, A.:
Towards Standardization of Data Licenses: The Montreal Data License,
arxiv [preprint], https://doi.org/10.48550/ARXIV.1903.12262, 2019.
a
Berkhout, V., Machill, B., and Reintanz, L.: Applications and platforms in
digitalisation of wind farm O&M – community feedback and survey results, J. Phys. Conf. Ser., 1669, 012006, https://doi.org/10.1088/1742-6596/1669/1/012006, 2020. a, b, c
Bird, L., Cochran, J., and Wang, X.: Wind and Solar Energy Curtailment:
Experience and Practices in the United States, Tech. Rep. NREL/TP-6A20-60983,
National Renewable Energy Laboratory, Golden, CO, USA, https://doi.org/10.2172/1126842,
2014. a
Branca, T. A., Fornai, B., Colla, V., Murri, M. M., Streppa, E., and
Schröder, A. J.: The Challenge of Digitalization in the Steel Sector,
Metals, 10, 288, https://doi.org/10.3390/met10020288, 2020. a
Branlard, E., Giardina, D., and Brown, C. S. D.: Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations, Wind Energ. Sci., 5, 1155–1167, https://doi.org/10.5194/wes-5-1155-2020, 2020. a
Brannstrom, C., Pasqualetti, M. J., Gorayeb, A., Wolsink, M., Slattery, M.,
Devine-Wright, P., and Sovacool, B.: Why ignore a major challenge of wind
energy science?, https://science.sciencemag.org/content/366/6464/eaau2027/tab-e-letters
(last access: 1 February 2023), 2019. a
Bugshan, H.: Co-innovation: the role of online communities, J.
Strateg. Market., 23, 175–186, https://doi.org/10.1080/0965254X.2014.920905, 2015. a
Buljan, A.: New Drone Prototype to Start Offshore Wind Trials Next Year,
https://www.offshorewind.biz/2020/12/10/new-drone-prototype-to-start-offshore-wind-trials-next-year/
(last access: 22 February 2022), 2020. a
Buntz, B.: Top 10 reasons people aren't embracing the IOT,
https://www.iotworldtoday.com/2016/04/20/top-10-reasons-people-aren-t-embracing-iot/
(last access: 22 February 2022), 2016. a
Bush, B., Brunhart-Lupo, N., Bugbee, B., Krishnan, V., Potter, K., and
Gruchalla, K.: Coupling visualization, simulation, and deep learning for
ensemble steering of complex energy models, in: 2017 IEEE Workshop on Data
Systems for Interactive Analysis (DSIA), 1–2 October 2017, Phoenix, AZ, USA, https://doi.org/10.1109/dsia.2017.8339087, 2017. a
Castro, G. D. R., Fernández, M. C. G., and Colsa, Á. U.: Unleashing the convergence amid digitalization and sustainability towards pursuing the Sustainable Development Goals (SDGs): A holistic review, J. Clean. Product., 280, 122204, https://doi.org/10.1016/j.jclepro.2020.122204, 2021. a
Chen, S., Kang, J., Liu, S., and Sun, Y.: Cognitive computing on unstructured
data for customer co-innovation, Eur. J. Market., 54, 570–593,
https://doi.org/10.1108/EJM-01-2019-0092, 2020. a
Chesbrough, H.: Business Model Innovation: Opportunities and Barriers, Long
Range Plan., 43, 354–363, https://doi.org/10.1016/j.lrp.2009.07.010, 2010. a
Christian Burmeister, D. L. and Piller, F. T.: Business Model Innovation for
Industrie 4.0: Why the “Industrial Internet” Mandates a New Perspective on
Innovation, Unternehmung, 70, 124–152, https://doi.org/10.5771/0042-059x-2016-2-124, 2016. a
Coronado, D. and Fischer, K.: Condition monitoring of wind turbines: State of
the art, user experience and recommendations, Tech. rep., Fraunhofer IWES,
Bremerhaven, Germany, https://publica.fraunhofer.de/documents/N-352558.html (last access: 1 February 2023), 2015. a
Dörenkämper, M., Olsen, B. T., Witha, B., Hahmann, A. N., Davis, N. N., Barcons, J., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Sastre-Marugán, M., Sīle, T., Trei, W., Žagar, M., Badger, J., Gottschall, J., Rodrigo, J. S., and Mann, J.: The Making of the New European Wind Atlas – Part 2: Production and evaluation, Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, 2020. a
Duffy, A., Hand, M., Wiser, R., Lantz, E., Dalla Riva, A., Berkhout, V.,
Stenkvist, M., Weir, D., and Lacal-Arántegui, R.: Land-based wind energy
cost trends in Germany, Denmark, Ireland, Norway, Sweden and the United
States, Appl. Energy, 277, 114777, https://doi.org/10.1016/j.apenergy.2020.114777, 2020. a
Dykes, K., Hand, M., Stehly, T., Veers, P., Robinson, M., Lantz, E., and
Tusing, R.: Enabling the SMART Wind Power Plant of the Future Through
Science-Based Innovation, Tech. Rep. NREL/TP-5000-68123, National Renewable
Energy Laboratory, Golden, CO, USA, https://www.nrel.gov/docs/fy17osti/68123.pdf (last access: 1 February 2023), 2017. a
Dykes, K. L., Veers, P. S., Lantz, E. J., Holttinen, H., Carlson, O., Tuohy,
A., Sempreviva, A. M., Clifton, A., Rodrigo, J. S., Berry, D. S., Laird, D.,
Carron, W. S., Moriarty, P. J., Marquis, M., Meneveau, C., Peinke, J.,
Paquette, J., Johnson, N., Pao, L., Fleming, P. A., Bottasso, C., Lehtomaki,
V., Robertson, A. N., Muskulus, M., Manwell, J., Tande, J. O., Sethuraman,
L., Roberts, J. O., and Fields, M. J.: IEA Wind TCP: Results of IEA Wind TCP
Workshop on a Grand Vision for Wind Energy Technology, Tech. rep., National
Renewable Energy Laboratory, https://doi.org/10.2172/1508509, 2019. a
Eggert, M., Stepputat, M., and Fluegge, W.: Digital Assistance in the
Maintenance of Offshore Wind Parks, J. Phys.: Conf. Ser., 1669, 012001, https://doi.org/10.1088/1742-6596/1669/1/012001, 2020. a
Eisenmann, T. R., Parker, G., and Alstyne, M. W. V.: Opening Platforms: How,
When and Why?, SSRN Electron. J., Working Paper 09-030, Harvard Business School, https://doi.org/10.2139/ssrn.1264012, 2008. a
European Commission: H2020 Programme: Guidelines on FAIR Data Management in
Horizon 2020,
https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf
(last access: 1 February 2023), 2016. a
European Commission: Turning FAIR into reality: final report and action plan
from the European Commission expert group on FAIR data, European Commission
Directorate General for Research and Innovation, https://doi.org/10.2777/1524, 2018. a
Feldman, T. and Peake, A.: End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning, arxiv [preprint], https://doi.org/10.48550/ARXIV.2104.02532, 2021. a
Fernández-Portillo, A., Almodóvar-González, M.,
Coca-Pérez, J. L., and Jiménez-Naranjo, H. V.: Is Sustainable
Economic Development Possible Thanks to the Deployment of ICT?,
Sustainability, 11, 6307, https://doi.org/10.3390/su11226307, 2019. a
Ferroukhi, R., Renner, M., and García-Baños, C.: Wind energy: A gender
perspective, International Renewable Energy Agency, Dubai,
https://www.irena.org/publications/2020/Jan/Wind-energy-A-gender-perspective (last access: 1 February 2023), 2020. a
Fields, M. J., Optis, M., Perr-Sauer, J., Todd, A., Lee, J. C. Y., Meissner,
J., Simley, E., Bodini, N., Williams, L., Sheng, S., and Hammond, R.: Wind
Plant Performance Prediction Benchmark Phase 1 Technical Report, Tech. Rep. NREL/TP-5000-78715, National Renewable Energy Laboratory, Golden, CO, USA, https://www.nrel.gov/docs/fy22osti/78715.pdf (last access: 1 February 2023), 2021. a, b
Fleming, P., Annoni, J., Shah, J. J., Wang, L., Ananthan, S., Zhang, Z.,
Hutchings, K., Wang, P., Chen, W., and Chen, L.: Field test of wake steering
at an offshore wind farm, Wind Energ. Sci., 2, 229–239,
https://doi.org/10.5194/wes-2-229-2017, 2017. a
Gauld, J., Bennun, L., Cook, A., Jobson, B., Oppel, S., Allinson, T. S.,
Franco, A., Gregory, R., Green, R., Humphreys, L., McCluskie, A., Petrovan,
S., Silva, J. P., Thaxter, C., Wischnewski, S., and Wright, L.: The fourth
grand challenge in the science of wind energy: minimizing biodiversity
impacts, https://science.sciencemag.org/content/366/6464/eaau2027/tab-e-letters
(last access: 1 February 2023), 2019. a
Geels, F. W.: Technological transitions as evolutionary reconfiguration
processes: a multi-level perspective and a case-study, Res. Policy, 31,
1257–1274, https://doi.org/10.1016/s0048-7333(02)00062-8, 2002. a, b
González-Aparicio, I. and Zucker, A.: Impact of wind power uncertainty
forecasting on the market integration of wind energy in Spain, Appl. Energy, 159, 334–349, https://doi.org/10.1016/j.apenergy.2015.08.104, 2015. a
Hahmann, A. N., Sīle, T., Witha, B., Davis, N. N., Dörenkämper, M., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Olsen, B. T., and Söderberg, S.: The making of the New European Wind Atlas – Part 1: Model sensitivity, Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, 2020. a
Heinz, J., Kennedy, J., Bradshaw, L., Raidt, J., Harding, M., Gurin, J.,
Wittes, B., and Cooper, R.: The Future of Data-Driven Innovation, Tech. rep.,
US Chamber of Commerce Foundation, Washington, DC,
https://www.uschamberfoundation.org/sites/default/files/Data Report Final 10.23.pdf
(last access: 1 February 2023), 2014. a
Holleran, S., Roscheck, F., Westermann, H., Fields, J., Kersting, G., Bohara,
A., Purdue, M., and Lee, J.: IEA-Task-43/digital_wra_data_standard:
1.0.0-2022.01, Zenodo [code], https://doi.org/10.5281/ZENODO.5841468, 2022. a, b
IEA Wind Task 43: IEA Wind Task 43 Open Data Catalogue,
https://www.ieawindtask43.org/proceedings-work-products/open-data-resources
(last access: 1 February 2023), 2022a. a
IEA Wind Task 43: IEA Wind Task 43 Glossary,
https://github.com/IEA-Task-43/glossary (last access: 1 February 2023), 2022b. a
Ignat, V.: Digitalization and the global technology trends, IOP Conf.
Ser.: Mater. Sci. Eng., 227, 012062, https://doi.org/10.1088/1757-899x/227/1/012062, 2017. a
IRENA: Renewable Energy Power Generation Costs in 2020, Tech. rep.,
International Renewable Energy Agency, Abu Dhabi, https://www.irena.org/publications/2021/Jun/Renewable-Power-Costs-in-2020 (last access: 1 February 2023), 2021. a
Isaacs, K. and Ancona, D.: 3 Ways to Build a Culture of Collaborative
Innovation,
https://hbr.org/2019/08/3-ways-to-build-a-culture-of-collaborative-innovation
(last access: 22 February 2022), 2019. a
Ishwarappa and Anuradha, J.: A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology, Proced. Comput. Sci., 48, 319–324, https://doi.org/10.1016/j.procs.2015.04.188, 2015. a
ISO: Industrial systems, installations and equipment and industrial products
– Structuring principles and reference designations – Part 10: Power
supply systems, Standard, ISO – International Organization for Standardization, Geneva, https://www.iso.org/standard/75471.html (last access: 1 February 2023), 2022. a
Jacobson, M. Z.: Roadmaps to Transition Countries to 100 % Clean, Renewable
Energy for All Purposes to Curtail Global Warming, Air Pollution, and Energy
Risk, Earth's Future, 5, 948–952, https://doi.org/10.1002/2017ef000672, 2017. a
Jenkel, L., Jonas, S., and Meyer, A.: Towards Fleet-wide Sharing of Wind
Turbine Condition Information through Privacy-preserving Federated Learning,
arxiv [preprint], https://doi.org/10.48550/ARXIV.2212.03529, 2022. a
Kapp, F., Matthes, N., Kruse, L., Niebeling, M., and Spangenberger, P.:
Fehlerdiagnose mit Virtual Reality trainieren – Entwicklung und Erprobung
einer virtuellen Offshore-Windenergieanlage, Z. Arbeitswissen., 76, 192–201, https://doi.org/10.1007/s41449-022-00316-8, 2022. a
Koukoura, S., Carroll, J., and McDonald, A.: On the use of AI based vibration condition monitoring of wind turbine gearboxes, J. Phys.: Conf. Ser., 1222, 012045, https://doi.org/10.1088/1742-6596/1222/1/012045, 2019. a, b
Kumar, N., Prakash, A., and Ding, Y.: Data Science for Wind Energy,
https://cran.r-project.org/web/packages/DSWE/ (last access: 22 February 2021), 2022. a
Lameh, S. F., Noble, W., Amannejad, Y., and Afshar, A.: Analysis of Federated
Learning as a Distributed Solution for Learning on Edge Devices, in: 2020
International Conference on Intelligent Data Science Technologies and
Applications (IDSTA), 19–22 October 2020, virtual, https://doi.org/10.1109/idsta50958.2020.9264060, 2020. a
Lee, J. C. Y., Stuart, P., Clifton, A., Fields, M. J., Perr-Sauer, J.,
Williams, L., Cameron, L., Geer, T., and Housley, P.: The Power Curve Working
Group's assessment of wind turbine power performance prediction methods, Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, 2020. a
Lee, S. M., Olson, D. L., and Trimi, S.: Co‐innovation: convergenomics,
collaboration, and co‐creation for organizational values, Manage. Decis., 50, 817–831, https://doi.org/10.1108/00251741211227528, 2012. a
Lund, H. and Sempreviva, A.: Semi-automatic taxonomy development for wind
energy data collections, in: Proceedings of the 2019 Wind Energy Science
Conference (WESC 2019), 17–20 June 2019, Cork, Ireland, https://doi.org/10.5281/zenodo.3368636, 2019. a, b
Maksimov, Y. V. and Fricker, S.: Licensing in Artificial Intelligence
Competitions and Consortium Project Collaborations, in: 2020 46th Euromicro
Conference on Software Engineering and Advanced Applications (SEAA), 26–28 August 2020, Portoroz, Slovenia, 292–301, https://doi.org/10.1109/SEAA51224.2020.00056, 2020. a
Marr, B.: Walt Disney Parks and Resorts: How Big Data is Transforming Our
Family Holidays, in: Big Data in Practice, John Wiley & Sons, Ltd, 211–215, https://doi.org/10.1002/9781119278825.ch33, 2016. a
Mathis, C.: Data Lakes, Datenbank-Spektrum, 17, 289–293,
https://doi.org/10.1007/s13222-017-0272-7, 2017. a
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A.:
Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Proc. Mach. Learn. Res., 54, 1273–1282, 2017. a
Mergel, I., Edelmann, N., and Haug, N.: Defining digital transformation:
Results from expert interviews, Govern. Inform. Quart., 36, 101385, https://doi.org/10.1016/j.giq.2019.06.002, 2019. a
Michiorri, A., Sempreviva, A. M., Philipp, S., Perez-Lopez, P., Ferriere, A.,
and Moser, D.: Topic Taxonomy and Metadata to Support Renewable Energy
Digitalisation, Energies, 15, 9531, https://doi.org/10.3390/en15249531, 2022. a
Moriarty, P., Rodrigo, J. S., Gancarski, P., Chuchfield, M., Naughton, J. W.,
Hansen, K. S., Machefaux, E., Maguire, E., Castellani, F., Terzi, L., Breton,
S.-P., and Ueda, Y.: IEA-Task 31 WAKEBENCH: Towards a protocol for wind
farm flow model evaluation. Part 2: Wind farm wake models, J. Phys.: Conf. Ser., 524, 012185, https://doi.org/10.1088/1742-6596/524/1/012185, 2014. a
Naeher, D.: Technology Adoption Under Costly Information Processing, Int. Econ. Rev., 63, 699–753, https://doi.org/10.1111/iere.12545, 2022. a
Nagasawa, T., Pillay, C., Beier, G., Fritzsche, K., Pougel, F., Takama, T.,
The, K., and Bobashev, I.: Accelerating clean energy through Industry 4.0:
manufacturing the next revolution, Tech. rep., UNIDO – United Nations Industrial Development Organization, Vienna, Austria, https://www.unido.org/sites/default/files/2017-08/REPORT_Accelerating_clean_energy_through_Industry_4.0.Final_0.pdf (last access: 1 February 2023), 2017. a
Nycander, E., Söder, L., Olauson, J., and Eriksson, R.: Curtailment analysis
for the Nordic power system considering transmission capacity, inertia limits
and generation flexibility, Renew. Energy, 152, 942–960, https://doi.org/10.1016/j.renene.2020.01.059, 2020. a
Palm, A.: Early adopters and their motives: Differences between earlier and
later adopters of residential solar photovoltaics, Renew. Sustain. Energ. Rev., 133, 110142, https://doi.org/10.1016/j.rser.2020.110142, 2020. a
Perr-Sauer, J., Optis, M., Fields, J. M., Bodini, N., Lee, J. C., Todd, A.,
Simley, E., Hammond, R., Phillips, C., Lunacek, M., Kemper, T., Williams, L.,
Craig, A., Agarwal, N., Sheng, S., and Meissner, J.: OpenOA: An Open-Source
Codebase For Operational Analysis of Wind Farms, J. Open Source Softw., 6, 2171, https://doi.org/10.21105/joss.02171, 2021. a
Pettas, V., Salari, M., Schlipf, D., and Cheng, P. W.: Investigation on the
potential of individual blade control for lifetime extension, J. Phys.: Conf. Ser., 1037, 032006, https://doi.org/10.1088/1742-6596/1037/3/032006, 2018. a, b
Pisano, G.: You Need an Innovation Strategy,
https://hbr.org/2015/06/you-need-an-innovation-strategy (last access: 22 February 2022), 2015. a
Rachinger, M., Rauter, R., Müller, C., Vorraber, W., and Schirgi, E.:
Digitalization and its influence on business model innovation, J. Manufact. Technol. Manage., 30, 1143–1160, https://doi.org/10.1108/jmtm-01-2018-0020, 2019. a, b, c
Reder, M., Yürüşen, N. Y., and Melero, J. J.: Data-driven
learning framework for associating weather conditions and wind turbine
failures, Reliabil. Eng. Syst. Safe., 169, 554–569,
https://doi.org/10.1016/j.ress.2017.10.004, 2018. a
Rezaei, M. M., Behzad, M., Haddadpour, H., and Moradi, H.: Development of a
reduced order model for nonlinear analysis of the wind turbine blade dynamics, Renew. Energy, 76, 264–282, https://doi.org/10.1016/j.renene.2014.11.021, 2015. a
Rinker, J. M., Hansen, M. H., and Larsen, T. J.: Calibrating a wind turbine
model using diverse datasets, J. Phys.: Conf. Ser., 1037, 062026, https://doi.org/10.1088/1742-6596/1037/6/062026, 2018. a
Robertson, A. N., Gueydon, S., Bachynski, E., Wang, L., Jonkman, J.,
Alarcón, D., Amet, E., Beardsell, A., Bonnet, P., Boudet, B., Brun, C.,
Chen, Z., Féron, M., Forbush, D., Galinos, C., Galvan, J., Gilbert, P.,
Gómez, J., Harnois, V., Haudin, F., Hu, Z., Dreff, J. L., Leimeister,
M., Lemmer, F., Li, H., Mckinnon, G., Mendikoa, I., Moghtadaei, A., Netzband,
S., Oh, S., Pegalajar-Jurado, A., Nguyen, M. Q., Ruehl, K., Schünemann,
P., Shi, W., Shin, H., Si, Y., Surmont, F., Trubat, P., Qwist, J., and
Wohlfahrt-Laymann, S.: OC6 Phase I: Investigating the underprediction of
low-frequency hydrodynamic loads and responses of a floating wind turbine, J. Phys.: Conf. Ser., 1618, 032033, https://doi.org/10.1088/1742-6596/1618/3/032033, 2020. a
Rodrigo, J. S., Gancarski, P., Arroyo, R. C., Moriarty, P., Chuchfield, M.,
Naughton, J. W., Hansen, K. S., Machefaux, E., Koblitz, T., Maguire, E.,
Castellani, F., Terzi, L., Breton, S.-P., Ueda, Y., Prospathopoulos, J.,
Oxley, G. S., Peralta, C., Zhang, X., and Witha, B.: IEA-Task 31
WAKEBENCH: Towards a protocol for wind farm flow model evaluation. Part 1:
Flow-over-terrain models, J. Phys.: Conf. Ser., 524, 012105, https://doi.org/10.1088/1742-6596/524/1/012105, 2014. a
Ross, P. S., Dearden, A., Araújo-Wang, C., Bejder, L., Reeves, R. R., Rose,
N. A., Winkler, R., Wright, A. J., and Wang, J. Y.: RE: Wind energy: Indirect
Cause of Extinction for Taiwanese white dolphins?,
https://science.sciencemag.org/content/366/6464/eaau2027/tab-e-letters
(last access: 1 February 2023), 2020. a
Schlipf, D., Hille, N., Raach, S., Scholbrock, A., and Simley, E.: IEA Wind
Task 32: Best Practices for the Certification of Lidar-Assisted Control
Applications, J. Phys.: Conf. Ser., 1102, 012010,
https://doi.org/10.1088/1742-6596/1102/1/012010, 2018. a
Schwanitz, V. J., Wierling, A., Biresselioglu, M. E., Celino, M., Demir, M. H., Bałazińska, M., Kruczek, M., Paier, M., and Suna, D.: Current state and call for action to accomplish findability, accessibility,
interoperability, and reusability of low carbon energy data, Sci. Rep., 12, 5208, https://doi.org/10.1038/s41598-022-08774-0, 2022. a
Schwardmann, U.: Digital Objects – FAIR Digital Objects: Which
Services Are Required?, Data Sci. J., 19, 15, https://doi.org/10.5334/dsj-2020-015, 2020. a
Sempreviva, A. M.: Find the data: Metadata and taxonomy for FAIR data sharing
in Wind Energy, presented at EERAdata online workshop & hackathon, 2–4 June 2020, online, Zenodo, https://doi.org/10.5281/ZENODO.5412367, 2020. a
Sempreviva, A. M., Vesth, A., Bak, C., Verelst, R. D., Giebel, G., Danielsen,
H. K., Mikkelsen, L. P., Andersson, M., Vasiljevic, N., Barth, S., Rodrigo,
J. S., Gancarski, P., Reigstad, T. I., Bolstad, H. C., Wagenaar, J. W., and
Hermans, W. K.: Taxonomy And Metadata For Wind Energy Research & Development, Zenodo, https://doi.org/10.5281/ZENODO.1199489, 2017. a, b
Sempreviva, A. M., Dimitrov, N., Vasiljevic, N., Davis, N., Lavanchy, P., and
Hüser, F.: Open science: sharing data, tools and workflows. A strategy to
inspire efficient collaboration, in: Proceedings of the WindEurope Conference
& Exhibition 2019 (WindEurope 2019), PO.094, Zenodo, https://doi.org/10.5281/ZENODO.2634496, 2019. a
Sethi, P. and Sarangi, S. R.: Internet of Things: Architectures, Protocols, and Applications, J. Elect. Comput. Eng., 2017, 1–25, https://doi.org/10.1155/2017/9324035, 2017. a
Simley, E., Millstein, D., Jeong, S., and Fleming, P.: The value of wake steering wind farm control in U.S. energy markets, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-12, in review, 2023. a, b
SKF: Maintenance 4.0 in wind farms: Bringing smart analytics to clean energy,
https://industrial-ai.skf.com/maintenance-4-0-in-wind-farms, last access: May 2021. a
Sorgner, A., Bode, E., and Krieger-Boden, C.: The Effects of Digitalization on the Gender Equality in the G20 economies, Tech. rep., Kiel Institute for the World Economy, Kiel, Germany,
https://www.emsdialogues.org/wp-content/uploads/2017/08/20170707_W20_Studie_v2.5.pdf
(last access: 1 February 2023), 2017. a
Sovacool, B. K. and Enevoldsen, P.: One style to build them all: Corporate
culture and innovation in the offshore wind industry, Energy Policy, 86,
402–415, https://doi.org/10.1016/j.enpol.2015.07.015, 2015. a, b
Statista: Digital Markets Insights: App – Worldwide,
https://www.statista.com/outlook/dmo/app/worldwide (last access:
1 February 2023), 2023a. a
Statista: Number of smartphones sold to end users worldwide from 2007 to 2021,
https://www.statista.com/statistics/263437/global-smartphone-sales-to-end-users-since-2007/
(last access: 1 February 2023), 2023b. a
Statista: Smartphone market revenue worldwide from 2013 to 2026,
https://www.statista.com/forecasts/1286699/worldwide-smartphone-market-revenue
(last access: 1 February 2023), 2023c. a
US Department Of Energy: FAIR data and models for Artificial Intelligence
And machine learning – Funding Opportunity Announcement (FOA) number:
DE-FOA-0002306,
https://science.osti.gov/-/media/grants/pdf/foas/2020/SC_FOA_0002306.pdf
(last access: 22 February 2022), 2020. a
van Kuik, G. A. M., Peinke, J., Nijssen, R., Lekou, D., Mann, J., Sørensen, J. N., Ferreira, C., van Wingerden, J. W., Schlipf, D., Gebraad, P., Polinder, H., Abrahamsen, A., van Bussel, G. J. W., Sørensen, J. D.,
Tavner, P., Bottasso, C. L., Muskulus, M., Matha, D., Lindeboom, H. J.,
Degraer, S., Kramer, O., Lehnhoff, S., Sonnenschein, M., Sørensen, P. E.,
Künneke, R. W., Morthorst, P. E., and Skytte, K.: Long-term research
challenges in wind energy – a research agenda by the European Academy of Wind Energy, Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, 2016. a, b
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O.,
Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki,
V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P.,
Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J.,
Robertson, A., Rodrigo, J. S., Sempreviva, A. M., Smith, J. C., Tuohy, A.,
and Wiser, R.: Grand challenges in the science of wind energy, Science, 366,
eaau2027, https://doi.org/10.1126/science.aau2027, 2019. a, b
Venkatesh, V. and Davis, F. D.: A Model of the Antecedents of Perceived Ease of Use: Development and Test, Decis. Sci., 27, 451–481,
https://doi.org/10.1111/j.1540-5915.1996.tb00860.x, 1996. a
Wang, B., Ha-Brookshire, J. E., and Bonifay, W.: Measuring Perceived Benefits
and Costs of New Technology Adoption in the Chinese Textile and Apparel
Industry, Cloth. Textil. Res. J., 40, 187–202, https://doi.org/10.1177/0887302X20969889, 2022. a
Wilczak, J., Finley, C., Freedman, J., Cline, J., Bianco, L., Olson, J.,
Djalalova, I., Sheridan, L., Ahlstrom, M., Manobianco, J., Zack, J., Carley,
J. R., Benjamin, S., Coulter, R., Berg, L. K., Mirocha, J., Clawson, K.,
Natenberg, E., and Marquis, M.: The Wind Forecast Improvement Project (WFIP):
A Public–Private Partnership Addressing Wind Energy Forecast Needs, B. Am. Meteorol. Soc., 96, 1699–1718, https://doi.org/10.1175/BAMS-D-14-00107.1, 2015. a
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M.,
Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E.,
Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O.,
Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J.,
Groth, P., Goble, C., Grethe, J. S., Heringa, J., 't Hoen, P. A., Hooft, R.,
Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer,
A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone,
S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A.,
Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester,
A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR
Guiding Principles for scientific data management and stewardship, Sci. Data, 3, 624646, https://doi.org/10.1038/sdata.2016.18, 2016. a
Wiser, R., Rand, J., Seel, J., Beiter, P., Baker, E., Lantz, E., and Gilman,
P.: Expert elicitation survey predicts 37 % to 49 % declines in wind
energy costs by 2050, Nat. Energy, 6, 555–565, https://doi.org/10.1038/s41560-021-00810-z, 2021. a, b
Wood, D.: Grand Challenges in Wind Energy Research, Front. Energ. Res., 8, 624646, https://doi.org/10.3389/fenrg.2020.624646, 2020. a
Würth, I., Valldecabres, L., Simon, E., Möhrlen, C., Uzunoğlu,
B., Gilbert, C., Giebel, G., Schlipf, D., and Kaifel, A.: Minute-Scale
Forecasting of Wind Power – Results from the Collaborative Workshop
of IEA Wind Task 32 and 36, Energies, 12, 712, https://doi.org/10.3390/en12040712,
2019. a
zenodo.org: Zenodo REST API,
https://zenodo.org/api/licenses/?page=1&size=1000, last access:
1 February 2023. a
Zhu, Y., Zhu, C., Song, C., Li, Y., Chen, X., and Yong, B.: Improvement of
reliability and wind power generation based on wind turbine real-time
condition assessment, Int. J. Elect. Power Energ. Syst., 113, 344–354, https://doi.org/10.1016/j.ijepes.2019.05.027, 2019. a
Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Wind energy creates huge amounts of data, which can be used to improve plant design, raise...
Altmetrics
Final-revised paper
Preprint