Articles | Volume 8, issue 8
https://doi.org/10.5194/wes-8-1341-2023
© 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-8-1341-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2023
Research article |
| 25 Aug 2023
| 25 Aug 2023
Enabling control co-design of the next generation of wind power plants
Andrew P. J. Stanley
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
now at: Shell International Exploration and Production Inc., Houston, TX 77079, USA
Christopher J. Bay
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Paul Fleming
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Related authors
Jared J. Thomas, Nicholas F. Baker, Paul Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John Jasa, Christopher Bay, Federico Tilli, David Bieniek, Nick Robinson, Andrew P. J. Stanley, Wesley Holt, and Andrew Ning
Wind Energ. Sci., 8, 865–891, https://doi.org/10.5194/wes-8-865-2023, https://doi.org/10.5194/wes-8-865-2023, 2023
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This work compares eight optimization algorithms (including gradient-based, gradient-free, and hybrid) on a wind farm optimization problem with 4 discrete regions, concave boundaries, and 81 wind turbines. Algorithms were each run by researchers experienced with that algorithm. Optimized layouts were unique but with similar annual energy production. Common characteristics included tightly-spaced turbines on the outer perimeter and turbines loosely spaced and roughly on a grid in the interior.
Andrew P. J. Stanley and Andrew Ning
Wind Energ. Sci., 4, 99–114, https://doi.org/10.5194/wes-4-99-2019, https://doi.org/10.5194/wes-4-99-2019, 2019
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We show that optimizing wind turbine design and wind turbine layout at the same time is superior to doing so sequentially. This coupled optimization can reduce the cost of energy by 2–5 % compared to sequential optimization. We also demonstrate that wind farms with closely spaced wind turbines can greatly benefit from different turbine designs throughout the farm. Heterogeneous turbine design can reduce the cost of energy by up to 10 % compared to farms with all identical turbines.
Eric Simley, Dev Millstein, Seongeun Jeong, and Paul Fleming
Wind Energ. Sci., 9, 219–234, https://doi.org/10.5194/wes-9-219-2024, https://doi.org/10.5194/wes-9-219-2024, 2024
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Wake steering is a wind farm control technology in which turbines are misaligned with the wind to deflect their wakes away from downstream turbines, increasing total power production. In this paper, we use a wind farm control model and historical electricity prices to assess the potential increase in market value from wake steering for 15 US wind plants. For most plants, we find that the relative increase in revenue from wake steering exceeds the relative increase in energy production.
Regis Thedin, Garrett Barter, Jason Jonkman, Rafael Mudafort, Christopher J. Bay, Kelsey Shaler, and Jasper Kreeft
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-6, https://doi.org/10.5194/wes-2024-6, 2024
Preprint under review for WES
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This work investigates asymmetries in terms of power performance and fatigue loading on a 5-turbine wind farm subject to wake steering strategies. Both the yaw misalignment angle and the wind direction were varied from negative to positive. We highlight conditions in which fatigue loading is lower while still maintenance good power gains and show that partial wake is the source of the asymmetries observed. We provide recommendations in terms of yaw misalignment angles for a given wind direction.
Jared J. Thomas, Nicholas F. Baker, Paul Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John Jasa, Christopher Bay, Federico Tilli, David Bieniek, Nick Robinson, Andrew P. J. Stanley, Wesley Holt, and Andrew Ning
Wind Energ. Sci., 8, 865–891, https://doi.org/10.5194/wes-8-865-2023, https://doi.org/10.5194/wes-8-865-2023, 2023
Short summary
Short summary
This work compares eight optimization algorithms (including gradient-based, gradient-free, and hybrid) on a wind farm optimization problem with 4 discrete regions, concave boundaries, and 81 wind turbines. Algorithms were each run by researchers experienced with that algorithm. Optimized layouts were unique but with similar annual energy production. Common characteristics included tightly-spaced turbines on the outer perimeter and turbines loosely spaced and roughly on a grid in the interior.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
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This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
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We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Michael J. LoCascio, Christopher J. Bay, Majid Bastankhah, Garrett E. Barter, Paul A. Fleming, and Luis A. Martínez-Tossas
Wind Energ. Sci., 7, 1137–1151, https://doi.org/10.5194/wes-7-1137-2022, https://doi.org/10.5194/wes-7-1137-2022, 2022
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This work introduces the FLOW Estimation and Rose Superposition (FLOWERS) wind turbine wake model. This model analytically integrates the wake over wind directions to provide a time-averaged flow field. This new formulation is used to perform layout optimization. The FLOWERS model provides a smooth flow field over an entire wind plant at fraction of the computational cost of the standard numerical integration approach.
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022, https://doi.org/10.5194/wes-7-741-2022, 2022
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In wind plants, turbines can be yawed to steer their wakes away from downstream turbines and achieve an increase in plant power. The yaw angles become expensive to solve for in large farms. This paper presents a new method to solve for the optimal turbine yaw angles in a wind plant. The yaw angles are defined as Boolean variables – each turbine is either yawed or nonyawed. With this formulation, most of the gains from wake steering can be reached with a large reduction in computational expense.
Jared J. Thomas, Christopher J. Bay, Andrew P. J. Stanley, and Andrew Ning
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-4, https://doi.org/10.5194/wes-2022-4, 2022
Revised manuscript not accepted
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We wanted to determine if and how optimization algorithms may be exploiting inaccuracies in the simple models used for wind farm layout optimization. Comparing optimization results from a simple model to large-eddy simulations showed that even a simple model provides enough information for optimizers to find good layouts. However, varying the number of wind directions in the optimization showed that the wind resource discretization can negatively impact the optimization results.
Andrew P. J. Stanley, Jennifer King, Christopher Bay, and Andrew Ning
Wind Energ. Sci., 7, 433–454, https://doi.org/10.5194/wes-7-433-2022, https://doi.org/10.5194/wes-7-433-2022, 2022
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In this paper, we present a computationally inexpensive model to calculate wind turbine blade fatigue caused by waking and partial waking. The model accounts for steady state on the blade, as well as wind turbulence. The model is fast enough to be used in wind farm layout optimization, which has not been possible with more expensive fatigue models in the past. The methods introduced in this paper will allow for farms with increased energy production that maintain turbine structural reliability.
Paul Fleming, Michael Sinner, Tom Young, Marine Lannic, Jennifer King, Eric Simley, and Bart Doekemeijer
Wind Energ. Sci., 6, 1521–1531, https://doi.org/10.5194/wes-6-1521-2021, https://doi.org/10.5194/wes-6-1521-2021, 2021
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The paper presents a new validation campaign of wake steering at a commercial wind farm. The campaign uses fixed yaw offset positions, rather than a table of optimal yaw offsets dependent on wind direction, to enable comparison with engineering models of wake steering. Additionally, by applying the same offset in beneficial and detrimental conditions, we are able to collect important data for assessing second-order wake model predictions.
Eric Simley, Paul Fleming, Nicolas Girard, Lucas Alloin, Emma Godefroy, and Thomas Duc
Wind Energ. Sci., 6, 1427–1453, https://doi.org/10.5194/wes-6-1427-2021, https://doi.org/10.5194/wes-6-1427-2021, 2021
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Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to deflect their low-velocity wakes away from downstream turbines, increasing overall power production. Here, we present results from a two-turbine wake-steering experiment at a commercial wind plant. By analyzing the wind speed dependence of wake steering, we find that the energy gained tends to increase for higher wind speeds because of both the wind conditions and turbine operation.
Andrew P. J. Stanley, Owen Roberts, Jennifer King, and Christopher J. Bay
Wind Energ. Sci., 6, 1143–1167, https://doi.org/10.5194/wes-6-1143-2021, https://doi.org/10.5194/wes-6-1143-2021, 2021
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Wind farm layout optimization is an essential part of wind farm design. In this paper, we present different methods to determine the number of turbines in a wind farm, as well as their placement. Also in this paper we explore the effect that the objective function has on the wind farm design and found that wind farm layout is highly sensitive to the objective. The optimal number of turbines can vary greatly, from 15 to 54 for the cases in this paper, depending on the metric that is optimized.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
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Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Jennifer King, Paul Fleming, Ryan King, Luis A. Martínez-Tossas, Christopher J. Bay, Rafael Mudafort, and Eric Simley
Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, https://doi.org/10.5194/wes-6-701-2021, 2021
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This paper highlights the secondary effects of wake steering, including yaw-added wake recovery and secondary steering. These effects enhance the value of wake steering especially when applied to a large wind farm. This paper models these secondary effects using an analytical model proposed in the paper. The results of this model are compared with large-eddy simulations for several cases including 2-turbine, 3-turbine, 5-turbine, and 38-turbine cases.
Luis A. Martínez-Tossas, Jennifer King, Eliot Quon, Christopher J. Bay, Rafael Mudafort, Nicholas Hamilton, Michael F. Howland, and Paul A. Fleming
Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, https://doi.org/10.5194/wes-6-555-2021, 2021
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In this paper a three-dimensional steady-state solver for flow through a wind farm is developed and validated. The computational cost of the solver is on the order of seconds for large wind farms. The model is validated using high-fidelity simulations and SCADA.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Jason Roadman, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci., 5, 1253–1272, https://doi.org/10.5194/wes-5-1253-2020, https://doi.org/10.5194/wes-5-1253-2020, 2020
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A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
Patrick Murphy, Julie K. Lundquist, and Paul Fleming
Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, https://doi.org/10.5194/wes-5-1169-2020, 2020
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We present and evaluate an improved method for predicting wind turbine power production based on measurements of the wind speed and direction profile across the rotor disk for a wind turbine in complex terrain. By comparing predictions to actual power production from a utility-scale wind turbine, we show this method is more accurate than methods based on hub-height wind speed or surface-based atmospheric characterization.
Paul Fleming, Jennifer King, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, David Jager, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, https://doi.org/10.5194/wes-5-945-2020, 2020
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This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in a companion paper (https://wes.copernicus.org/articles/4/273/2019/). The authors implemented wake steering on two turbine pairs and compared results with the latest FLORIS model of wake steering, showing good agreement in overall energy increase.
Eric Simley, Paul Fleming, and Jennifer King
Wind Energ. Sci., 5, 451–468, https://doi.org/10.5194/wes-5-451-2020, https://doi.org/10.5194/wes-5-451-2020, 2020
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Wind farm wake losses occur when turbines operate in the wakes of upstream turbines. However, wake steering control can be used to deflect wakes away from downstream turbines. A method for including wind direction variability in wake steering simulations is presented here. Controller performance is shown to improve when wind direction variability is accounted for. Furthermore, the importance of wind direction variability is shown for different turbine spacings and atmospheric conditions.
Daniel S. Zalkind, Gavin K. Ananda, Mayank Chetan, Dana P. Martin, Christopher J. Bay, Kathryn E. Johnson, Eric Loth, D. Todd Griffith, Michael S. Selig, and Lucy Y. Pao
Wind Energ. Sci., 4, 595–618, https://doi.org/10.5194/wes-4-595-2019, https://doi.org/10.5194/wes-4-595-2019, 2019
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We present a model that both (1) reduces the computational effort involved in analyzing design trade-offs and (2) provides a qualitative understanding of the root cause of fatigue and extreme structural loads for wind turbine components from the blades to the tower base. We use this model in conjunction with design loads from high-fidelity simulations to analyze and compare the trade-offs between power capture and structural loading for large rotor concepts.
Jennifer Annoni, Christopher Bay, Kathryn Johnson, Emiliano Dall'Anese, Eliot Quon, Travis Kemper, and Paul Fleming
Wind Energ. Sci., 4, 355–368, https://doi.org/10.5194/wes-4-355-2019, https://doi.org/10.5194/wes-4-355-2019, 2019
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Typically, turbines do not share information with nearby turbines in a wind farm. Relying on a single turbine sensor on the back of a turbine nacelle can lead to large errors in yaw misalignment or excessive yawing due to noisy sensor measurements. The wind farm consensus control approach in this paper shows the benefits of sharing information between nearby turbines by computing a robust estimate of the wind direction using noisy sensor information from these neighboring turbines.
Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, https://doi.org/10.5194/wes-4-273-2019, 2019
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Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
Christopher J. Bay, Jennifer King, Paul Fleming, Rafael Mudafort, and Luis A. Martínez-Tossas
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-19, https://doi.org/10.5194/wes-2019-19, 2019
Preprint withdrawn
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This work details a new low-fidelity wake model to be used in determining operational strategies for wind turbines. With the additional physics that this model captures, optimizations have found new control strategies that provide greater increases in performance than previously determined, and these performance increases have been confirmed in high-fidelity simulations. As such, this model can be used in the design and optimization of future wind farms and operational schemes.
Luis A. Martínez-Tossas, Jennifer Annoni, Paul A. Fleming, and Matthew J. Churchfield
Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, https://doi.org/10.5194/wes-4-127-2019, 2019
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A new control-oriented model is developed to compute the wake of a wind turbine under yaw. The model uses a simplified version of the Navier–Stokes equation with assumptions. Good agreement is found between the model-proposed and large eddy simulations of a wind turbine in yaw.
Andrew P. J. Stanley and Andrew Ning
Wind Energ. Sci., 4, 99–114, https://doi.org/10.5194/wes-4-99-2019, https://doi.org/10.5194/wes-4-99-2019, 2019
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We show that optimizing wind turbine design and wind turbine layout at the same time is superior to doing so sequentially. This coupled optimization can reduce the cost of energy by 2–5 % compared to sequential optimization. We also demonstrate that wind farms with closely spaced wind turbines can greatly benefit from different turbine designs throughout the farm. Heterogeneous turbine design can reduce the cost of energy by up to 10 % compared to farms with all identical turbines.
Jennifer Annoni, Paul Fleming, Andrew Scholbrock, Jason Roadman, Scott Dana, Christiane Adcock, Fernando Porte-Agel, Steffen Raach, Florian Haizmann, and David Schlipf
Wind Energ. Sci., 3, 819–831, https://doi.org/10.5194/wes-3-819-2018, https://doi.org/10.5194/wes-3-819-2018, 2018
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This paper addresses the modeling aspect of wind farm control. To implement successful wind farm controls, a suitable model has to be used that captures the relevant physics. This paper addresses three different wake models that can be used for controls and compares these models with lidar field data from a utility-scale turbine.
Paul Fleming, Jennifer Annoni, Matthew Churchfield, Luis A. Martinez-Tossas, Kenny Gruchalla, Michael Lawson, and Patrick Moriarty
Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, https://doi.org/10.5194/wes-3-243-2018, 2018
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This paper investigates the role of flow structures in wind farm control through yaw misalignment. A pair of counter-rotating vortices is shown to be important in deforming the shape of the wake. Further, we demonstrate that the vortex structures created in wake steering can enable a greater change power generation than currently modeled in control-oriented models. We propose that wind farm controllers can be made more effective if designed to take advantage of these effects.
Rick Damiani, Scott Dana, Jennifer Annoni, Paul Fleming, Jason Roadman, Jeroen van Dam, and Katherine Dykes
Wind Energ. Sci., 3, 173–189, https://doi.org/10.5194/wes-3-173-2018, https://doi.org/10.5194/wes-3-173-2018, 2018
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The paper discusses load effects on wind turbines operating under misaligned-flow operations, which is part of a strategy to optimize wind-power-plant power production, where upwind turbines can be rotated off the wind axis to redirect their wakes. Analytical simplification, aeroelastic simulations, and field data from an instrumented turbine are compared and interpreted to provide an informed picture on the loads for various components.
Paul Fleming, Jennifer Annoni, Jigar J. Shah, Linpeng Wang, Shreyas Ananthan, Zhijun Zhang, Kyle Hutchings, Peng Wang, Weiguo Chen, and Lin Chen
Wind Energ. Sci., 2, 229–239, https://doi.org/10.5194/wes-2-229-2017, https://doi.org/10.5194/wes-2-229-2017, 2017
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In this paper, a field test of wake-steering control is presented. In the campaign, an array of turbines within an operating commercial offshore wind farm have the normal yaw controller modified to implement wake steering according to a yaw control strategy. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture.
Related subject area
Thematic area: Wind technologies | Topic: Design concepts and methods for plants, turbines, and components
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Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 697–719, https://doi.org/10.5194/wes-9-697-2024, https://doi.org/10.5194/wes-9-697-2024, 2024
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Wind farms at sea are becoming more densely clustered, which means that next to individual wind turbines interfering with each other in a single wind farm also interference between wind farms becomes important. Using a climate model, this study shows that the efficiency of wind farm clusters and the interference between the wind farms in the cluster depend strongly on the properties of the individual wind farms and are also highly sensitive to the spacing between the wind farms.
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
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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.
Amalia Ida Hietanen, Thor Heine Snedker, Katherine Dykes, and Ilmas Bayati
Wind Energ. Sci., 9, 417–438, https://doi.org/10.5194/wes-9-417-2024, https://doi.org/10.5194/wes-9-417-2024, 2024
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The layout of a floating offshore wind farm was optimized to maximize the relative net present value (NPV). By modeling power generation, losses, inter-array cables, anchors and operational costs, an increase of EUR 34.5 million in relative NPV compared to grid-based layouts was achieved. A sensitivity analysis was conducted to examine the impact of economic factors, providing valuable insights. This study contributes to enhancing the efficiency and cost-effectiveness of floating wind farms.
Daniel Ribnitzky, Frederik Berger, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci., 9, 359–383, https://doi.org/10.5194/wes-9-359-2024, https://doi.org/10.5194/wes-9-359-2024, 2024
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This paper provides an innovative blade design methodology for offshore wind turbines with very large rotors compared to their rated power, which are tailored for an increased power feed-in at low wind speeds. Rather than designing the blade for a single optimized operational point, we include the application of peak shaving in the design process and introduce a design for two tip speed ratios. We describe how enlargement of the rotor diameter can be realized to improve the value of wind power.
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
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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.
Fiona Dominique Lüdecke, Martin Schmid, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-13, https://doi.org/10.5194/wes-2024-13, 2024
Revised manuscript accepted for WES
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Large direct drive wind turbines, with a multi-megawatt power rating, face design challenges. Moving towards a more system-oriented design approach could potentially reduce mass and costs. Exploiting the full design space, though, may invoke interaction mechanisms, which have been neglected in the past. Based on coupled simulations, this work derives a better understanding of the electro-mechanical interaction mechanisms and identifies potential for design relevance.
Aiguo Zhou, Jinlei Shi, Tao Dong, Yi Ma, and Zhenhui Weng
Wind Energ. Sci., 9, 49–64, https://doi.org/10.5194/wes-9-49-2024, https://doi.org/10.5194/wes-9-49-2024, 2024
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This paper explores the nonlinear influence of the virtual mass mechanism on the test system in blade biaxial tests. The blade theory and simulation model are established to reveal the nonlinear amplitude–frequency characteristics of the blade-virtual-mass system. Increasing the amplitude of the blade or decreasing the seesaw length will lower the resonance frequency and load of the system. The virtual mass also affects the blade biaxial trajectory.
Xiaodong Zhang and Nikolay Dimitrov
Wind Energ. Sci., 8, 1613–1623, https://doi.org/10.5194/wes-8-1613-2023, https://doi.org/10.5194/wes-8-1613-2023, 2023
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Wind turbine extreme response estimation based on statistical extrapolation necessitates using a small number of simulations to calculate a low exceedance probability. This is a challenging task especially if we require small prediction error. We propose the use of a Gaussian mixture model as it is capable of estimating a low exceedance probability with minor bias error, even with limited simulation data, having flexibility in modeling the distributions of varying response variables.
Arne Bartschat, Karsten Behnke, and Matthias Stammler
Wind Energ. Sci., 8, 1495–1510, https://doi.org/10.5194/wes-8-1495-2023, https://doi.org/10.5194/wes-8-1495-2023, 2023
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Blade bearings are among the most stressed and challenging components of a wind turbine. Experimental investigations using different test rigs and real-size blade bearings have been able to show that rather short time intervals of only several hours of turbine operation can cause wear damage on the raceways of blade bearings. The proposed methods can be used to assess wear-critical operation conditions and to validate control strategies as well as lubricants for the application.
Juan-Andrés Pérez-Rúa, Mathias Stolpe, and Nicolaos Antonio Cutululis
Wind Energ. Sci., 8, 1453–1473, https://doi.org/10.5194/wes-8-1453-2023, https://doi.org/10.5194/wes-8-1453-2023, 2023
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With the challenges of ensuring secure energy supplies and meeting climate targets, wind energy is on course to become the cornerstone of decarbonized energy systems. This work proposes a new method to optimize wind farms by means of smartly placing wind turbines within a given project area, leading to more green-energy generation. This method performs satisfactorily compared to state-of-the-art approaches in terms of the resultant annual energy production and other high-level metrics.
Andrea Gamberini, Thanasis Barlas, Alejandro Gomez Gonzalez, and Helge Aagaard Madsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-63, https://doi.org/10.5194/wes-2023-63, 2023
Revised manuscript accepted for WES
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Movable surfaces on wind turbine (WT) blades, called active flaps, can reduce the cost of wind energy. However, they still need extensive testing. This study shows that the computer model used to design a WT with flaps aligns well with measurements obtained from a 3 month test on a commercial WT featuring a prototype flap. Particularly during flap actuation, there were minimal differences between simulated and measured data. These findings assure the reliability of WT designs incorporating flaps
Maaike Sickler, Bart Ummels, Michiel Zaaijer, Roland Schmehl, and Katherine Dykes
Wind Energ. Sci., 8, 1225–1233, https://doi.org/10.5194/wes-8-1225-2023, https://doi.org/10.5194/wes-8-1225-2023, 2023
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This paper investigates the effect of wind farm layout on the performance of offshore wind farms. A regular farm layout is compared to optimised irregular layouts. The irregular layouts have higher annual energy production, and the power production is less sensitive to wind direction. However, turbine towers require thicker walls to counteract increased fatigue due to increased turbulence levels in the farm. The study shows that layout optimisation can be used to maintain high-yield performance.
Nicholas Peters, Christopher Silva, and John Ekaterinaris
Wind Energ. Sci., 8, 1201–1223, https://doi.org/10.5194/wes-8-1201-2023, https://doi.org/10.5194/wes-8-1201-2023, 2023
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Wind turbines have increasingly been leveraged as a viable approach for obtaining renewable energy. As such, it is essential that engineers have a high-fidelity, low-cost approach to modeling rotor load distributions. In this study, such an approach is proposed. This modeling approach was shown to make high-fidelity predictions at a low computational cost for rotor distributed-pressure loads as rotor geometry varied, allowing for an optimization of the rotor to be completed.
Paul Veers, Carlo L. Bottasso, Lance Manuel, Jonathan Naughton, Lucy Pao, Joshua Paquette, Amy Robertson, Michael Robinson, Shreyas Ananthan, Thanasis Barlas, Alessandro Bianchini, Henrik Bredmose, Sergio González Horcas, Jonathan Keller, Helge Aagaard Madsen, James Manwell, Patrick Moriarty, Stephen Nolet, and Jennifer Rinker
Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, https://doi.org/10.5194/wes-8-1071-2023, 2023
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Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
Jenna Iori, Carlo Luigi Bottasso, and Michael Kenneth McWilliam
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-58, https://doi.org/10.5194/wes-2023-58, 2023
Revised manuscript accepted for WES
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The controller of a wind turbine has an important role to regulate the power production and avoid failure of the structure. However, it is often designed after the rest of the turbine and thus its potential is not fully exploited. An alternative is to design the structure and the controller simultaneously. This work develops a method to identify if a given turbine design can benefit from this new simultaneous design process. For example, higher and cheaper turbine tower can be built this way.
Kisorthman Vimalakanthan, Harald van der Mijle Meijer, Iana Bakhmet, and Gerard Schepers
Wind Energ. Sci., 8, 41–69, https://doi.org/10.5194/wes-8-41-2023, https://doi.org/10.5194/wes-8-41-2023, 2023
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Leading edge erosion (LEE) is one of the most critical degradation mechanisms that occur with wind turbine blades. A detailed understanding of the LEE process and the impact on aerodynamic performance due to the damaged leading edge is required to optimize blade maintenance. Providing accurate modeling tools is therefore essential. This novel study assesses CFD approaches for modeling high-resolution scanned LE surfaces from an actual blade with LEE damages.
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
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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.
Alessandro Bianchini, Galih Bangga, Ian Baring-Gould, Alessandro Croce, José Ignacio Cruz, Rick Damiani, Gareth Erfort, Carlos Simao Ferreira, David Infield, Christian Navid Nayeri, George Pechlivanoglou, Mark Runacres, Gerard Schepers, Brent Summerville, David Wood, and Alice Orrell
Wind Energ. Sci., 7, 2003–2037, https://doi.org/10.5194/wes-7-2003-2022, https://doi.org/10.5194/wes-7-2003-2022, 2022
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The paper is part of the Grand Challenges Papers for Wind Energy. It provides a status of small wind turbine technology in terms of technical maturity, diffusion, and cost. Then, five grand challenges that are thought to be key to fostering the development of the technology are proposed. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Improvement areas are highlighted, within which 10 key enabling actions are finally proposed to the wind community.
Mads H. Aa. Madsen, Frederik Zahle, Sergio González Horcas, Thanasis K. Barlas, and Niels N. Sørensen
Wind Energ. Sci., 7, 1471–1501, https://doi.org/10.5194/wes-7-1471-2022, https://doi.org/10.5194/wes-7-1471-2022, 2022
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This work presents a shape optimization framework based on computational fluid dynamics. The design framework is used to optimize wind turbine blade tips for maximum power increase while avoiding that extra loading is incurred. The final results are shown to align well with related literature. The resulting tip shape could be mounted on already installed wind turbines as a sleeve-like solution or be conceived as part of a modular blade with tips designed for site-specific conditions.
Edward Hart, Adam Stock, George Elderfield, Robin Elliott, James Brasseur, Jonathan Keller, Yi Guo, and Wooyong Song
Wind Energ. Sci., 7, 1209–1226, https://doi.org/10.5194/wes-7-1209-2022, https://doi.org/10.5194/wes-7-1209-2022, 2022
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We consider characteristics and drivers of loads experienced by wind turbine main bearings using simplified models of hub and main-bearing configurations. Influences of deterministic wind characteristics are investigated for 5, 7.5, and 10 MW turbine models. Load response to gusts and wind direction changes are also considered. Cubic load scaling is observed, veer is identified as an important driver of load fluctuations, and strong links between control and main-bearing load response are shown.
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Short summary
Better wind farms can be built by simultaneously optimizing turbine locations and control, which is currently impossible or extremely challenging because of the size of the problem. The authors present a method to determine optimal wind farm control as a function of the turbine locations, which enables turbine layout and control to be optimized together by drastically reducing the size of the problem. In an example, a wind farm's performance improves by 0.8 % when optimized with the new method.
Better wind farms can be built by simultaneously optimizing turbine locations and control, which...
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