Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-741-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-741-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2022
Research article |
| 31 Mar 2022
| 31 Mar 2022
Fast yaw optimization for wind plant wake steering using Boolean yaw angles
National Renewable Energy Laboratory, National Wind Technology Center, Boulder, CO 80303, USA
Christopher Bay
National Renewable Energy Laboratory, National Wind Technology Center, Boulder, CO 80303, USA
Rafael Mudafort
National Renewable Energy Laboratory, National Wind Technology Center, Boulder, CO 80303, USA
Paul Fleming
National Renewable Energy Laboratory, National Wind Technology Center, Boulder, CO 80303, USA
Related authors
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Andrew P. J. Stanley and Andrew Ning
Wind Energ. Sci., 4, 663–676, https://doi.org/10.5194/wes-4-663-2019, https://doi.org/10.5194/wes-4-663-2019, 2019
Short summary
Short summary
When designing a wind farm, one crucial step is finding the correct location or optimizing the location of the wind turbines to maximize power production. In the past, optimizing the turbine layout of large wind farms has been difficult because of the large number of interacting variables. In this paper, we present the boundary-grid parameterization method, which defines the layout of any wind farm with only five variables, allowing people to study and design wind farms regardless of the size.
Andrés Santiago Padrón, Jared Thomas, Andrew P. J. Stanley, Juan J. Alonso, and Andrew Ning
Wind Energ. Sci., 4, 211–231, https://doi.org/10.5194/wes-4-211-2019, https://doi.org/10.5194/wes-4-211-2019, 2019
Short summary
Short summary
We propose the use of a new method to efficiently compute the annual energy production (AEP) of a wind farm by properly handling the uncertainties in the wind direction and wind speed. We apply the new ideas to the layout optimization of a large wind farm. We show significant computational savings by reducing the number of simulations required to accurately compute and optimize the AEP of different wind farms.
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
Short summary
Short summary
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
Revised manuscript accepted for WES
Short summary
Short summary
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.
Andrew P. J. Stanley, Christopher J. Bay, and Paul Fleming
Wind Energ. Sci., 8, 1341–1350, https://doi.org/10.5194/wes-8-1341-2023, https://doi.org/10.5194/wes-8-1341-2023, 2023
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Andrew P. J. Stanley and Andrew Ning
Wind Energ. Sci., 4, 663–676, https://doi.org/10.5194/wes-4-663-2019, https://doi.org/10.5194/wes-4-663-2019, 2019
Short summary
Short summary
When designing a wind farm, one crucial step is finding the correct location or optimizing the location of the wind turbines to maximize power production. In the past, optimizing the turbine layout of large wind farms has been difficult because of the large number of interacting variables. In this paper, we present the boundary-grid parameterization method, which defines the layout of any wind farm with only five variables, allowing people to study and design wind farms regardless of the size.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Andrés Santiago Padrón, Jared Thomas, Andrew P. J. Stanley, Juan J. Alonso, and Andrew Ning
Wind Energ. Sci., 4, 211–231, https://doi.org/10.5194/wes-4-211-2019, https://doi.org/10.5194/wes-4-211-2019, 2019
Short summary
Short summary
We propose the use of a new method to efficiently compute the annual energy production (AEP) of a wind farm by properly handling the uncertainties in the wind direction and wind speed. We apply the new ideas to the layout optimization of a large wind farm. We show significant computational savings by reducing the number of simulations required to accurately compute and optimize the AEP of different wind farms.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Design methods, reliability and uncertainty modelling
Effectively using multifidelity optimization for wind turbine design
Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling
A simplified, efficient approach to hybrid wind and solar plant site optimization
Influence of wind turbine design parameters on linearized physics-based models in OpenFAST
Input torque measurements for wind turbine gearboxes using fiber-optic strain sensors
A model to calculate fatigue damage caused by partial waking during wind farm optimization
A fully integrated optimization framework for designing a complex geometry offshore wind turbine spar-type floating support structure
Land-based wind turbines with flexible rail-transportable blades – Part 2: 3D finite element design optimization of the rotor blades
Local-thermal-gradient and large-scale-circulation impacts on turbine-height wind speed forecasting over the Columbia River Basin
Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine
Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble
What are the benefits of lidar-assisted control in the design of a wind turbine?
Design procedures and experimental verification of an electro-thermal deicing system for wind turbines
Land-based wind turbines with flexible rail-transportable blades – Part 1: Conceptual design and aeroservoelastic performance
Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant
Aeroelastic loads on a 10 MW turbine exposed to extreme events selected from a year-long large-eddy simulation over the North Sea
Optimal scheduling of the next preventive maintenance activity for a wind farm
A method for preliminary rotor design – Part 1: Radially Independent Actuator Disc model
A method for preliminary rotor design – Part 2: Wind turbine Optimization with Radial Independence
Wind farm layout optimization using pseudo-gradients
On the scaling of wind turbine rotors
Reducing cost uncertainty in the drivetrain design decision with a focus on the operational phase
Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks
Wind tunnel comparison of four VAWT configurations to test load-limiting concept and CFD validation
Redesign of an upwind rotor for a downwind configuration: design changes and cost evaluation
Fatigue lifetime calculation of wind turbine blade bearings considering blade-dependent load distribution
Reliability analysis of offshore wind turbine foundations under lateral cyclic loading
Operational-based annual energy production uncertainty: are its components actually uncorrelated?
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models
Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations
A surrogate model approach for associating wind farm load variations with turbine failures
New strategies for optimized structural monitoring of wind farms: experimental campaign
Differences in damping of edgewise whirl modes operating an upwind turbine in a downwind configuration
Assessment of a rotor blade extension retrofit as a supplement to the lifetime extension of wind turbines
Is the Blade Element Momentum theory overestimating wind turbine loads? – An aeroelastic comparison between OpenFAST's AeroDyn and QBlade's Lifting-Line Free Vortex Wake method
Development and feasibility study of segment blade test methodology
Analytical model for the power–yaw sensitivity of wind turbines operating in full wake
Wake steering optimization under uncertainty
Radar-derived precipitation climatology for wind turbine blade leading edge erosion
WESgraph: a graph database for the wind farm domain
Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling
Optimal relationship between power and design-driving loads for wind turbine rotors using 1-D models
Digitalization of scanning lidar measurement campaign planning
Massive simplification of the wind farm layout optimization problem
System-level design studies for large rotors
Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads
Performance of non-intrusive uncertainty quantification in the aeroservoelastic simulation of wind turbines
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
Multipoint high-fidelity CFD-based aerodynamic shape optimization of a 10 MW wind turbine
Comparison between upwind and downwind designs of a 10 MW wind turbine rotor
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022, https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
Short summary
Using highly accurate simulations within a design cycle is prohibitively computationally expensive. We implement and present a multifidelity optimization method and showcase its efficacy using three different case studies. We examine aerodynamic blade design, turbine controls tuning, and a wind plant layout problem. In each case, the multifidelity method finds an optimal design that performs better than those obtained using simplified models but at a lower cost than high-fidelity optimization.
Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, and Gerard Schepers
Wind Energ. Sci., 7, 759–781, https://doi.org/10.5194/wes-7-759-2022, https://doi.org/10.5194/wes-7-759-2022, 2022
Short summary
Short summary
An accurate prediction of loads and power of an offshore wind turbine is needed for an optimal design. However, such predictions are typically performed with engineering models that contain many inaccuracies and uncertainties. In this paper we have proposed a systematic approach to quantify and calibrate these uncertainties based on two experimental datasets. The calibrated models are much closer to the experimental data and are equipped with an estimate of the uncertainty in the predictions.
Charles Tripp, Darice Guittet, Jennifer King, and Aaron Barker
Wind Energ. Sci., 7, 697–713, https://doi.org/10.5194/wes-7-697-2022, https://doi.org/10.5194/wes-7-697-2022, 2022
Short summary
Short summary
Hybrid solar and wind plant layout optimization is a difficult, complex problem. In this paper, we propose a parameterized approach to wind and solar hybrid power plant layout optimization that greatly reduces problem dimensionality while guaranteeing that the generated layouts have a desirable regular structure. We demonstrate that this layout method that generates high-performance, regular layouts which respect hard constraints (e.g., placement restrictions).
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022, https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary
Short summary
This paper summarizes efforts done to understand the impact of design parameter variations in the physical system (e.g., mass, stiffness, geometry, aerodynamic, and hydrodynamic coefficients) on the linearized system using OpenFAST in support of the development of the WEIS toolset to enable controls co-design of floating offshore wind turbines.
Unai Gutierrez Santiago, Alfredo Fernández Sisón, Henk Polinder, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 505–521, https://doi.org/10.5194/wes-7-505-2022, https://doi.org/10.5194/wes-7-505-2022, 2022
Short summary
Short summary
The gearbox is one of the main contributors to the overall cost of wind energy, and it is acknowledged that we still do not fully understand its loading. The study presented in this paper develops a new alternative method to measure input rotor torque in wind turbine gearboxes, overcoming the drawbacks related to measuring on a rotating shaft. The method presented in this paper could make measuring gearbox torque more cost-effective, which would facilitate its adoption in serial wind turbines.
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
Short summary
Short summary
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.
Mareike Leimeister, Maurizio Collu, and Athanasios Kolios
Wind Energ. Sci., 7, 259–281, https://doi.org/10.5194/wes-7-259-2022, https://doi.org/10.5194/wes-7-259-2022, 2022
Short summary
Short summary
Floating offshore wind technology has high potential but still faces challenges for gaining economic competitiveness to allow commercial market uptake. Hence, design optimization plays a key role; however, the final optimum floater obtained highly depends on the specified optimization problem. Thus, by considering alternative structural realization approaches, not very stringent limitations on the structure and dimensions are required. This way, more innovative floater designs can be captured.
Ernesto Camarena, Evan Anderson, Josh Paquette, Pietro Bortolotti, Roland Feil, and Nick Johnson
Wind Energ. Sci., 7, 19–35, https://doi.org/10.5194/wes-7-19-2022, https://doi.org/10.5194/wes-7-19-2022, 2022
Short summary
Short summary
The length of rotor blades of land-based wind turbines is currently constrained by logistics. Turbine manufacturers currently propose segmented solutions to overcome these limits, but blade joints come with extra masses and costs. This work investigates an alternative solution, namely the design of ultra-flexible blades that can be transported on rail via controlled bending. The results show that this is a promising pathway to further increasing the size of land-based wind turbines.
Ye Liu, Yun Qian, and Larry K. Berg
Wind Energ. Sci., 7, 37–51, https://doi.org/10.5194/wes-7-37-2022, https://doi.org/10.5194/wes-7-37-2022, 2022
Short summary
Short summary
Uncertainties in initial conditions (ICs) decrease the accuracy of wind speed forecasts. We find that IC uncertainties can alter wind speed by modulating the weather system. IC uncertainties in local thermal gradient and large-scale circulation jointly contribute to wind speed forecast uncertainties. Wind forecast accuracy in the Columbia River Basin is confined by initial uncertainties in a few specific regions, providing useful information for more intense measurement and modeling studies.
Alessandro Croce, Stefano Cacciola, and Luca Sartori
Wind Energ. Sci., 7, 1–17, https://doi.org/10.5194/wes-7-1-2022, https://doi.org/10.5194/wes-7-1-2022, 2022
Short summary
Short summary
In recent years, research has focused on the development of wind farm controllers with the aim of minimizing interactions between machines and thus improving the production of the wind farm.
In this work we have analyzed the effects of these recent technologies on a single wind turbine, with the aim of understanding the impact of these controllers on the design of the machine itself.
The analyses have shown there are non-negligible effects on some components of the wind turbine.
Nicola Bodini, Weiming Hu, Mike Optis, Guido Cervone, and Stefano Alessandrini
Wind Energ. Sci., 6, 1363–1377, https://doi.org/10.5194/wes-6-1363-2021, https://doi.org/10.5194/wes-6-1363-2021, 2021
Short summary
Short summary
We develop two machine-learning-based approaches to temporally extrapolate uncertainty in hub-height wind speed modeled by a numerical weather prediction model. We test our approaches in the California Outer Continental Shelf, where a significant offshore wind energy development is currently being planned, and we find that both provide accurate results.
Helena Canet, Stefan Loew, and Carlo L. Bottasso
Wind Energ. Sci., 6, 1325–1340, https://doi.org/10.5194/wes-6-1325-2021, https://doi.org/10.5194/wes-6-1325-2021, 2021
Short summary
Short summary
Lidar-assisted control (LAC) is used to redesign the rotor and tower of three turbines, differing in terms of wind class, size, and power rating. The load reductions enabled by LAC are used to save
mass, increase hub height, or extend lifetime. The first two strategies yield reductions in the cost of energy only for the tower of the largest machine, while more interesting benefits are obtained for lifetime extension.
David Getz and Jose Palacios
Wind Energ. Sci., 6, 1291–1309, https://doi.org/10.5194/wes-6-1291-2021, https://doi.org/10.5194/wes-6-1291-2021, 2021
Short summary
Short summary
A methodology to design electrothermal deicing protection for wind turbines is presented. The method relies on modeling and experimental testing to determine the critical ice thickness. The critical ice thickness needed is dependent on the ice tensile strength, which varies with icing conditions. The ice tensile strength must be overcome by the stress that a de-bonded ice structure exerts under centrifugal force at its root region, where it attaches to a non-de-bonded ice region.
Pietro Bortolotti, Nick Johnson, Nikhar J. Abbas, Evan Anderson, Ernesto Camarena, and Joshua Paquette
Wind Energ. Sci., 6, 1277–1290, https://doi.org/10.5194/wes-6-1277-2021, https://doi.org/10.5194/wes-6-1277-2021, 2021
Short summary
Short summary
The length of rotor blades of land-based wind turbines is currently constrained by logistics. Turbine manufacturers currently propose segmented solutions to overcome these limits, but blade joints come with extra masses and costs. This work investigates an alternative solution, namely the design of ultra-flexible blades that can be transported on rail via controlled bending. The results show that this is a promising pathway for further increasing the size of land-based wind turbines.
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
Short summary
Short summary
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.
Gerard Schepers, Pim van Dorp, Remco Verzijlbergh, Peter Baas, and Harmen Jonker
Wind Energ. Sci., 6, 983–996, https://doi.org/10.5194/wes-6-983-2021, https://doi.org/10.5194/wes-6-983-2021, 2021
Short summary
Short summary
In this article the aeroelastic loads on a 10 MW turbine in response to unconventional wind conditions selected from a year-long large-eddy simulation on a site at the North Sea are evaluated. Thereto an assessment is made of the practical importance of these wind conditions within an aeroelastic context based on high-fidelity wind modelling. Moreover the accuracy of BEM-based methods for modelling such wind conditions is assessed.
Quanjiang Yu, Michael Patriksson, and Serik Sagitov
Wind Energ. Sci., 6, 949–959, https://doi.org/10.5194/wes-6-949-2021, https://doi.org/10.5194/wes-6-949-2021, 2021
Short summary
Short summary
There are two ways to maintain a multi-component system: corrective maintenance, when a broken component is replaced with a new one, and preventive maintenance (PM), when some components are replaced in a planned manner before they break down. This article proposes a mathematical model for finding an optimal time to perform the next PM activity and selecting the components which should be replaced. The model is fast to solve, and it can be used as a key module in a maintenance scheduling app.
Kenneth Loenbaek, Christian Bak, Jens I. Madsen, and Michael McWilliam
Wind Energ. Sci., 6, 903–915, https://doi.org/10.5194/wes-6-903-2021, https://doi.org/10.5194/wes-6-903-2021, 2021
Short summary
Short summary
We present a model for assessing the aerodynamic performance of a wind turbine rotor through a different parametrization of the classical blade element momentum model. The model establishes an analytical relationship between the loading in the flow direction and the power along the rotor span. The main benefit of the model is the ease with which it can be applied for rotor optimization and especially load constraint power optimization.
Kenneth Loenbaek, Christian Bak, and Michael McWilliam
Wind Energ. Sci., 6, 917–933, https://doi.org/10.5194/wes-6-917-2021, https://doi.org/10.5194/wes-6-917-2021, 2021
Short summary
Short summary
A novel wind turbine rotor optimization methodology is presented. Using an assumption of radial independence it is possible to obtain the Pareto-optimal relationship between power and loads through the use of KKT multipliers, leaving an optimization problem that can be solved at each radial station independently. Combining it with a simple cost function it is possible to analytically solve for the optimal power per cost with given inputs for the aerodynamics and the cost function.
Erik Quaeghebeur, René Bos, and Michiel B. Zaaijer
Wind Energ. Sci., 6, 815–839, https://doi.org/10.5194/wes-6-815-2021, https://doi.org/10.5194/wes-6-815-2021, 2021
Short summary
Short summary
We present a technique to support the optimal layout (placement) of wind turbines in a wind farm. It efficiently determines good directions and distances for moving turbines. An improved layout reduces production losses and so makes the farm project economically more attractive. Compared to most existing techniques, our approach requires less time. This allows wind farm designers to explore more alternatives and provides the flexibility to adapt the layout to site-specific requirements.
Helena Canet, Pietro Bortolotti, and Carlo L. Bottasso
Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, https://doi.org/10.5194/wes-6-601-2021, 2021
Short summary
Short summary
The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
Freia Harzendorf, Ralf Schelenz, and Georg Jacobs
Wind Energ. Sci., 6, 571–584, https://doi.org/10.5194/wes-6-571-2021, https://doi.org/10.5194/wes-6-571-2021, 2021
Short summary
Short summary
Making wind turbines more reliable over their lifetime is an important goal for improving wind turbine technology. The wind turbine drivetrain has a major influence on turbine reliability. This paper presents an approach that will help to identify holistically better drivetrain concepts in an early product design phase from an operational perspective as it is able to estimate and assess drivetrain-concept-specific inherent risks in the operational phase.
Artur Movsessian, Marcel Schedat, and Torsten Faber
Wind Energ. Sci., 6, 539–554, https://doi.org/10.5194/wes-6-539-2021, https://doi.org/10.5194/wes-6-539-2021, 2021
Short summary
Short summary
The assessment of the structural condition and technical lifetime extension of a wind turbine is challenging due to lack of information for the estimation of fatigue loads. This paper demonstrates the modelling of damage-equivalent loads of the fore–aft bending moments of a wind turbine tower, highlighting the advantage of using the neighbourhood component analysis. This feature selection technique is compared to correlation analysis, stepwise regression, and principal component analysis.
Jan Wiśniewski, Krzysztof Rogowski, Konrad Gumowski, and Jacek Szumbarski
Wind Energ. Sci., 6, 287–294, https://doi.org/10.5194/wes-6-287-2021, https://doi.org/10.5194/wes-6-287-2021, 2021
Short summary
Short summary
The article describes results of experimental wind tunnel and CFD testing of four different straight-bladed vertical axis wind turbine model configurations. The experiment tested a novel concept of vertically dividing and azimuthally shifting a turbine rotor into two parts with a specific uneven height division in order to limit cycle amplitudes and average cycle values of bending moments at the bottom of the turbine shaft to increase product lifetime, especially for industrial-scale turbines.
Gesine Wanke, Leonardo Bergami, Frederik Zahle, and David Robert Verelst
Wind Energ. Sci., 6, 203–220, https://doi.org/10.5194/wes-6-203-2021, https://doi.org/10.5194/wes-6-203-2021, 2021
Short summary
Short summary
This article regards a rotor redesign for a wind turbine in upwind and in downwind rotor configurations. A simple optimization tool is used to estimate the aerodynamic planform, as well as the structural mass distribution of the rotor blade. The designs are evaluated in full load base calculations according to the IEC standard with the aeroelastic tool HAWC2. A scaling model is used to scale turbine and energy costs from the design loads and compare the costs for the turbine configurations.
Oliver Menck, Matthias Stammler, and Florian Schleich
Wind Energ. Sci., 5, 1743–1754, https://doi.org/10.5194/wes-5-1743-2020, https://doi.org/10.5194/wes-5-1743-2020, 2020
Short summary
Short summary
Blade bearings of wind turbines experience unusual loads compared to bearings in other industrial applications, which adds some difficulty to the application of otherwise well-established calculation methods, like fatigue lifetime. As a result, different methods for such calculations can be found in the literature. This paper compares three approaches of varying complexity and comes to the conclusion that the simplest of the methods is very inaccurate compared to the more complex methods.
Gianluca Zorzi, Amol Mankar, Joey Velarde, John D. Sørensen, Patrick Arnold, and Fabian Kirsch
Wind Energ. Sci., 5, 1521–1535, https://doi.org/10.5194/wes-5-1521-2020, https://doi.org/10.5194/wes-5-1521-2020, 2020
Short summary
Short summary
Storms, typhoons or seismic actions are likely to cause permanent rotation of offshore wind turbine foundations. Excessive rotation jeopardizes the operation of the wind turbine. In this study geotechnical, loads and probabilistic modelling are used to develop a reliability framework for predicting the rotation of the foundation under cyclic lateral loading.
Nicola Bodini and Mike Optis
Wind Energ. Sci., 5, 1435–1448, https://doi.org/10.5194/wes-5-1435-2020, https://doi.org/10.5194/wes-5-1435-2020, 2020
Short summary
Short summary
Calculations of annual energy production (AEP) and its uncertainty are critical for wind farm financial transactions. Standard industry practice assumes that different uncertainty categories within an AEP calculation are uncorrelated and can therefore be combined through a sum of squares approach. In this project, we show the limits of this assumption by performing operational AEP estimates for over 470 wind farms in the United States and propose a more accurate way to combine uncertainties.
Simon Letzgus
Wind Energ. Sci., 5, 1375–1397, https://doi.org/10.5194/wes-5-1375-2020, https://doi.org/10.5194/wes-5-1375-2020, 2020
Short summary
Short summary
One of the major challenges when working with wind turbine sensor data in practice is the presence of systematic changes in signal behaviour induced by malfunctions or maintenance actions. We found that approximately every third signal is affected by such change points and introduce an algorithm which reliably detects them in a highly automated fashion. The algorithm enables the application of data-driven techniques to monitor wind turbine components using data from commonly installed sensors.
Emmanuel Branlard, Dylan Giardina, and Cameron S. D. Brown
Wind Energ. Sci., 5, 1155–1167, https://doi.org/10.5194/wes-5-1155-2020, https://doi.org/10.5194/wes-5-1155-2020, 2020
Short summary
Short summary
The paper presents an application of the Kalman filtering technique to estimate loads on a wind turbine. The approach combines a mechanical model and a set of measurements to estimate signals that are not available in the measurements, such as wind speed, thrust, tower position, and tower loads. The model is severalfold faster than real time and is intended to be run online, for instance, to evaluate real-time fatigue life consumption of a field turbine using a digital twin.
Laura Schröder, Nikolay Krasimirov Dimitrov, and David Robert Verelst
Wind Energ. Sci., 5, 1007–1022, https://doi.org/10.5194/wes-5-1007-2020, https://doi.org/10.5194/wes-5-1007-2020, 2020
Short summary
Short summary
We suggest a methodology for correlating loads with component reliability of turbines in wind farms by combining physical modeling with machine learning. The suggested approach is demonstrated on an offshore wind farm for comparing performance, loads and lifetime estimations against recorded main bearing failures from maintenance reports. It is found that turbines positioned at the border of the wind farm with a higher expected AEP are estimated to experience earlier main bearing failures.
João Pacheco, Silvina Guimarães, Carlos Moutinho, Miguel Marques, José Carlos Matos, and Filipe Magalhães
Wind Energ. Sci., 5, 983–996, https://doi.org/10.5194/wes-5-983-2020, https://doi.org/10.5194/wes-5-983-2020, 2020
Short summary
Short summary
This paper introduces the Tocha wind farm, presents the different layouts adopted in the instrumentation of the wind turbines and shows initial results. At this preliminary stage, the capabilities of the very extensive monitoring layout are demonstrated. The results presented demonstrate the ability of the different monitoring components to track the modal parameters of the system, composed of tower and rotor, and to characterize the internal loads at the tower base and blade roots.
Gesine Wanke, Leonardo Bergami, and David Robert Verelst
Wind Energ. Sci., 5, 929–944, https://doi.org/10.5194/wes-5-929-2020, https://doi.org/10.5194/wes-5-929-2020, 2020
Short summary
Short summary
Converting an upwind wind turbine into a downwind configuration is shown to come with higher edgewise loads due to lower edgewise damping. The study shows from modal displacements of a reduced-order turbine model that the interaction between the forces on the rotor, the rotor motion, and the tower torsion is the main reason for the observed damping decrease.
Malo Rosemeier and Matthias Saathoff
Wind Energ. Sci., 5, 897–909, https://doi.org/10.5194/wes-5-897-2020, https://doi.org/10.5194/wes-5-897-2020, 2020
Short summary
Short summary
A huge number of wind turbines have reached their designated lifetime of 20 years.
Most of the turbines installed were overdesigned.
In practice, these turbines could potentially operate longer to increase the energy yield.
For the use case turbine considered in this work, a simple lifetime extension of 8.7 years increases the energy yield by 43.5 %. When the swept rotor area is increased by means of a blade tip extension, the yield is increased by an additional 2.3 %.
Sebastian Perez-Becker, Francesco Papi, Joseph Saverin, David Marten, Alessandro Bianchini, and Christian Oliver Paschereit
Wind Energ. Sci., 5, 721–743, https://doi.org/10.5194/wes-5-721-2020, https://doi.org/10.5194/wes-5-721-2020, 2020
Short summary
Short summary
Aeroelastic design load calculations play a key role in determining the design loads of the different wind turbine components. This study compares load estimations from calculations using a Blade Element Momentum aerodynamic model with estimations from calculations using a higher-order Lifting-Line Free Vortex Wake aerodynamic model. The paper finds and explains the differences in fatigue and extreme turbine loads for power production simulations that cover a wide range of turbulent wind speeds.
Kwangtae Ha, Moritz Bätge, David Melcher, and Steffen Czichon
Wind Energ. Sci., 5, 591–599, https://doi.org/10.5194/wes-5-591-2020, https://doi.org/10.5194/wes-5-591-2020, 2020
Short summary
Short summary
This paper outlines a novel segment test methodology for wind turbine rotor blades. It mainly aims at improving the efficiency of the fatigue test as a future test method at Fraunhofer IWES. The numerical simulation reveals that this method has a significant time savings of up to 43 % and 52 % for 60 and 90 m blades, while improving test quality within an acceptable range of overload. This test methodology could be a technical solution for future offshore rotor blades longer than 100 m.
Jaime Liew, Albert M. Urbán, and Søren Juhl Andersen
Wind Energ. Sci., 5, 427–437, https://doi.org/10.5194/wes-5-427-2020, https://doi.org/10.5194/wes-5-427-2020, 2020
Short summary
Short summary
In wind farms, the interaction between neighboring turbines can cause notable power losses. The focus of the paper is on how the combination of turbine yaw misalignment and wake effects influences the power loss in a wind turbine. The results of the paper show a more notable power loss due to turbine misalignment when turbines are closely spaced. The presented conclusions enable better predictions of a turbine's power production, which can assist the wind farm design process.
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.
Frederick Letson, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 5, 331–347, https://doi.org/10.5194/wes-5-331-2020, https://doi.org/10.5194/wes-5-331-2020, 2020
Short summary
Short summary
Wind turbine blade leading edge erosion (LEE) is potentially a significant source of energy loss and expense for wind farm operators. This study presents a novel approach to characterizing LEE potential from precipitation across the contiguous USA based on publicly available National Weather Service dual-polarization RADAR data. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments.
Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, and Michiel B. Zaaijer
Wind Energ. Sci., 5, 259–284, https://doi.org/10.5194/wes-5-259-2020, https://doi.org/10.5194/wes-5-259-2020, 2020
Short summary
Short summary
The design and management of an offshore wind farm involve expertise in many disciplines. It is hard for a single person to maintain the overview needed. Therefore, we have created WESgraph, a knowledge base for the wind farm domain, implemented as a graph database. It stores descriptions of the multitude of domain concepts and their various interconnections. It allows users to explore the domain and search for relationships within and across disciplines, enabling various applications.
Lars Einar S. Stieng and Michael Muskulus
Wind Energ. Sci., 5, 171–198, https://doi.org/10.5194/wes-5-171-2020, https://doi.org/10.5194/wes-5-171-2020, 2020
Short summary
Short summary
We present a framework for reducing the cost of support structures for offshore wind turbines that takes into account the many uncertainties that go into the design process. The results demonstrate how an efficient new approach, tailored for support structure design, allows the state of the art for design without uncertainties to be used within a framework that does include these uncertainties. This allows more realistic, and less conservative, design methods
to be used for practical design.
Kenneth Loenbaek, Christian Bak, Jens I. Madsen, and Bjarke Dam
Wind Energ. Sci., 5, 155–170, https://doi.org/10.5194/wes-5-155-2020, https://doi.org/10.5194/wes-5-155-2020, 2020
Short summary
Short summary
From the basic aerodynamic theory of wind turbine rotors, it is a well-known fact that there is a relationship between the loading of the rotor and power efficiency. It shows that there is a loading that maximizes the power efficiency, and it is common to target this maximum when designing rotors. But in this paper it is found that for rotors constrained by a load, the maximum power is found by decreasing the loading and increasing the rotor radius. Max power efficiency is therefore not optimal.
Nikola Vasiljević, Andrea Vignaroli, Andreas Bechmann, and Rozenn Wagner
Wind Energ. Sci., 5, 73–87, https://doi.org/10.5194/wes-5-73-2020, https://doi.org/10.5194/wes-5-73-2020, 2020
Short summary
Short summary
A WindScanner system consisting of two synchronized scanning lidars potentially represents a cost-effective solution for multipoint measurements. However, the lidar limitations and the site limitations are detrimental to the installation of lidars and number and location of measurement positions. To simplify the process of finding suitable measurement positions and lidar installation locations, a campaign planning workflow was devised. The paper describes the workflow and how it was digitalized.
Andrew P. J. Stanley and Andrew Ning
Wind Energ. Sci., 4, 663–676, https://doi.org/10.5194/wes-4-663-2019, https://doi.org/10.5194/wes-4-663-2019, 2019
Short summary
Short summary
When designing a wind farm, one crucial step is finding the correct location or optimizing the location of the wind turbines to maximize power production. In the past, optimizing the turbine layout of large wind farms has been difficult because of the large number of interacting variables. In this paper, we present the boundary-grid parameterization method, which defines the layout of any wind farm with only five variables, allowing people to study and design wind farms regardless of the size.
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
Short summary
Short summary
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.
Amy N. Robertson, Kelsey Shaler, Latha Sethuraman, and Jason Jonkman
Wind Energ. Sci., 4, 479–513, https://doi.org/10.5194/wes-4-479-2019, https://doi.org/10.5194/wes-4-479-2019, 2019
Short summary
Short summary
This paper identifies the most sensitive parameters for the load response of a 5 MW wind turbine. Two sets of parameters are examined: one set relating to the wind excitation characteristics and a second related to the physical properties of the wind turbine. The two sensitivity analyses are done separately, and the top most-sensitive parameters are identified for different load outputs throughout the structure. The findings will guide future validation campaigns and measurement needs.
Pietro Bortolotti, Helena Canet, Carlo L. Bottasso, and Jaikumar Loganathan
Wind Energ. Sci., 4, 397–406, https://doi.org/10.5194/wes-4-397-2019, https://doi.org/10.5194/wes-4-397-2019, 2019
Short summary
Short summary
The paper studies the effects of uncertainties in aeroservoelastic
wind turbine models. Uncertainties are associated with the wind
inflow characteristics and the blade surface state, and they are propagated
by means of two non-intrusive methods throughout the
aeroservoelastic model of a large conceptual offshore wind
turbine. Results are compared with a brute-force extensive Monte
Carlo sampling to assess the convergence characteristics of the
non-intrusive approaches.
Andrés Santiago Padrón, Jared Thomas, Andrew P. J. Stanley, Juan J. Alonso, and Andrew Ning
Wind Energ. Sci., 4, 211–231, https://doi.org/10.5194/wes-4-211-2019, https://doi.org/10.5194/wes-4-211-2019, 2019
Short summary
Short summary
We propose the use of a new method to efficiently compute the annual energy production (AEP) of a wind farm by properly handling the uncertainties in the wind direction and wind speed. We apply the new ideas to the layout optimization of a large wind farm. We show significant computational savings by reducing the number of simulations required to accurately compute and optimize the AEP of different wind farms.
Mads H. Aa. Madsen, Frederik Zahle, Niels N. Sørensen, and Joaquim R. R. A. Martins
Wind Energ. Sci., 4, 163–192, https://doi.org/10.5194/wes-4-163-2019, https://doi.org/10.5194/wes-4-163-2019, 2019
Short summary
Short summary
The wind energy industry relies heavily on CFD to analyze new designs. This paper investigates a way to utilize CFD further upstream the design process where lower-fidelity methods are used. We present the first comprehensive 3-D CFD adjoint-based shape optimization of a 10 MW modern offshore wind turbine. The present work shows that, with the right tools, we can model the entire geometry, including the root, and optimize modern wind turbine rotors at the cost of a few hundred CFD evaluations.
Pietro Bortolotti, Abhinav Kapila, and Carlo L. Bottasso
Wind Energ. Sci., 4, 115–125, https://doi.org/10.5194/wes-4-115-2019, https://doi.org/10.5194/wes-4-115-2019, 2019
Short summary
Short summary
The paper compares upwind and downwind three-bladed configurations
for a 10 MW wind turbine in terms of power and loads. For the
downwind case, the study also considers a load-aligned solution
with active coning. Results indicate that downwind solutions are
slightly more advantageous than upwind ones, although improvements
are small. Additionally, pre-alignment is difficult to achieve in
practice, and the active coning solution is associated with very
significant engineering challenges.
Cited articles
Adaramola, M. and Krogstad, P.-Å.: Experimental investigation of wake
effects on wind turbine performance, Renew. Energy, 36, 2078–2086, 2011. a
Administration, U. E. I.: Electric power monthly with data for April 2021,
Table ES1.A, Tech. rep., US Department of Energy, https://www.eia.gov/electricity/monthly/ (last access: 28 March 2022), 2021a. a
Administration, U. E. I.: Monthly energy review, Table 7.2a, Tech. Rep. DOE/EIA-0035 (2021/6), Office of Energy Statistics, US Department of Energy, https://www.eia.gov/totalenergy/data/monthly/archive/00352106.pdf (last access: 28 March 2022), 2021b. a
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of
wind turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541, 2016. a
Crespo, A., Herna, J., Crespo, A., and Hernandez, J.: Turbulence characteristics in wind-turbine wakes, J. Wind Eng. Indust. Aerodynam., 61, 71–85, 1996. a
Damiani, R., Dana, S., Annoni, J., Fleming, P., Roadman, J., van Dam, J., and Dykes, K.: Assessment of wind turbine component loads under yaw-offset conditions, Wind Energ. Sci., 3, 173–189, https://doi.org/10.5194/wes-3-173-2018, 2018. a
Doekemeijer, B. M., van der Hoek, D., and van Wingerden, J.-W.: Closed-loop
model-based wind farm control using FLORIS under time-varying inflow
conditions, Renew. Energy, 156, 719–730, 2020. a
Dou, B., Qu, T., Lei, L., and Zeng, P.: Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model, Energy, 209, 118415, https://doi.org/10.1016/j.energy.2020.118415, 2020. a, b
Fleming, P., Churchfield, M., Scholbrock, A., Clifton, A., Schreck, S.,
Johnson, K., Wright, A., Gebraad, P., Annoni, J., Naughton, B., and Berg, J.: Detailed field test of yaw-based wake steering, J. Phys.: Conf. Ser., 753, 052003, https://doi.org/10.1088/1742-6596/753/5/052003, 2016a. a
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
Fleming, P., King, J., Dykes, K., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Lopez, H., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1, Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, 2019. a
Gebraad, P. M., Teeuwisse, F., van Wingerden, J.-W., Fleming, P. A., Ruben, S. D., Marden, J. R., and Pao, L. Y.: A data-driven model for wind plant
power optimization by yaw control, in: IEEE 2014 American Control Conference,
4–6 June 2014, Portland, OR, USA, 3128–3134, https://doi.org/10.1109/ACC.2014.6859118, 2014. a, b
Gebraad, P. M., Teeuwisse, F., Van Wingerden, J., Fleming, P. A., Ruben, S.,
Marden, J., and Pao, L.: Wind plant power optimization through yaw control
using a parametric model for wake effects – a CFD simulation study, Wind
Energy, 19, 95–114, 2016. a
Gill, P. E., Murray, W., and Saunders, M. A.: SNOPT: An SQP algorithm for
large-scale constrained optimization, SIAM Rev., 47, 99–131, 2005. a
Grady, S., Hussaini, M., and Abdullah, M. M.: Placement of wind turbines using genetic algorithms, Renew. Energy, 30, 259–270, 2005. a
Howland, M. F., Bossuyt, J., Martínez-Tossas, L. A., Meyers, J., and
Meneveau, C.: Wake structure in actuator disk models of wind turbines in yaw
under uniform inflow conditions, J. Renew. Sustain. Energ., 8, 043301, https://doi.org/10.1063/1.4955091 2016. a
Jensen, N. O.: A note on wind generator interaction, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.456.4080&rep=rep1&type=pdf
(last access: 28 March 2022), 1983. a
Kanev, S.: Dynamic wake steering and its impact on wind farm power production
and yaw actuator duty, Renew. Energy, 146, 9–15, 2020. a
Katic, I., Højstrup, J., and Jensen, N. O.: A simple model for cluster
efficiency, in: vol. 1, European wind energy association conference and exhibition, A. Raguzzi, Rome, Italy, 407–410, https://backend.orbit.dtu.dk/ws/portalfiles/portal/106427419/A_Simple_Model_for_Cluster_Efficiency_EWEC_86_.pdf
(last access: 28 March 2022), 1986. a
King, J., Fleming, P., King, R., Martínez-Tossas, L. A., Bay, C. J., Mudafort, R., and Simley, E.: Control-oriented model for secondary effects of wake steering, Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, 2021. a
Kragh, K. A. and Hansen, M. H.: Load alleviation of wind turbines by yaw
misalignment, Wind Energy, 17, 971–982, 2014. a
Lin, M. and Porté-Agel, F.: Power Maximization and Fatigue-Load Mitigation in a Wind-turbine Array by Active Yaw Control: an LES Study, J.
Phys.: Conf. Ser., 1618, 042036, https://doi.org/10.1088/1742-6596/1618/4/042036, 2020. a
Lyu, Z., Xu, Z., and Martins, J.: Benchmarking optimization algorithms for wing aerodynamic design optimization, in: vol. 11,Proceedings of the 8th International Conference on Computational Fluid Dynamics, Chengdu, Sichuan, China, https://mdolab.engin.umich.edu/bibliography/Lyu2014f
(last access: 28 March 2022), 2014. a
Marmidis, G., Lazarou, S., and Pyrgioti, E.: Optimal placement of wind turbines in a wind park using Monte Carlo simulation, Renew. Energy, 33,
1455–1460, 2008. a
Mosetti, G., Poloni, C., and Diviacco, B.: Optimization of wind turbine
positioning in large windfarms by means of a genetic algorithm, J. Wind Eng. Indust. Aerodynam., 51, 105–116, 1994. a
National Renewable Energy Laboratory: Floris, GitHub [code], https://github.com/NREL/floris/releases/tag/v2.4, last access: 29 March 2022. a
Ning, A. and Petch, D.: Integrated design of downwind land-based wind turbines using analytic gradients, Wind Energy, 19, 2137–2152, 2016. a
NREL: FLORIS, Version 2.4, GitHub, https://github.com/NREL/floris/releases/tag/v2.4 (last access: 28 March 2022), 2021. a
Park, J., Kwon, S., and Law, K. H.: Wind farm power maximization based on a
cooperative static game approach, in: vol. 8688, Active and Passive Smart Structures and Integrated Systems 2013, International Society for Optics and Photonics, p. 86880R, https://doi.org/10.1117/12.2009618, 2013. a
Quick, J., King, J., King, R. N., Hamlington, P. E., and Dykes, K.: Wake steering optimization under uncertainty, Wind Energ. Sci., 5, 413–426, https://doi.org/10.5194/wes-5-413-2020, 2020. a, b
Rios, L. M. and Sahinidis, N. V.: Derivative-free optimization: a review of
algorithms and comparison of software implementations, J. Global Optimiz., 56, 1247–1293, 2013. a
Simley, E., Fleming, P., and King, J.: Design and analysis of a wake steering controller with wind direction variability, Wind Energ. Sci., 5, 451–468, https://doi.org/10.5194/wes-5-451-2020, 2020. a
Simley, E., Fleming, P., Girard, N., Alloin, L., Godefroy, E., and Duc, T.: Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, Wind Energ. Sci., 6, 1427–1453, https://doi.org/10.5194/wes-6-1427-2021, 2021. a
Stanley, A. P. J.: stanley2021-yaw-optimimization, Zenodo [data set and code], https://doi.org/10.5281/zenodo.6395099, 2022. a
Stanley, A. P. J. and Ning, A.: Coupled wind turbine design and layout optimization with nonhomogeneous wind turbines, Wind Energ. Sci., 4, 99–114, https://doi.org/10.5194/wes-4-99-2019, 2019. a
Stokes, L. C. and Warshaw, C.: Renewable energy policy design and framing
influence public support in the United States, Nat. Energy, 2, 1–6, 2017. a
Thomas, J. J. and Ning, A.: A method for reducing multi-modality in the wind
farm layout optimization problem, J. Phys.: Conf. Ser., 1037, 042012, https://doi.org/10.1088/1742-6596/1037/4/042012, 2018.
a
Thomas, J. J., Gebraad, P. M., and Ning, A.: Improving the FLORIS wind plant
model for compatibility with gradient-based optimization, Wind Eng., 41, 313–329, 2017. 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, 2021. a
Wu, N., Kenway, G., Mader, C. A., Jasa, J., and Martins, J. R.: pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems, J. Open Source Softw., 5, 2564, https://doi.org/10.21105/joss.02564, 2020. a
Wu, N., Kenwa, G., Mader, C. A., Jasa, J., and Martins, J. R. R. A.:
Pyoptsparse, GitHub [code],
https://github.com/mdolab/pyoptsparse, last access: 29 March 2022. a
Zingg, D. W., Nemec, M., and Pulliam, T. H.: A comparative evaluation of
genetic and gradient-based algorithms applied to aerodynamic optimization,
Eur. J. Comput. Mech./Revue Européenne de Mécanique Numérique, 17, 103–126, 2008. a
Short summary
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.
In wind plants, turbines can be yawed to steer their wakes away from downstream turbines and...
Altmetrics
Final-revised paper
Preprint