Articles | Volume 9, issue 7
https://doi.org/10.5194/wes-9-1507-2024
© Author(s) 2024. 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-9-1507-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jul 2024
Research article |
| 12 Jul 2024
| 12 Jul 2024
Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm
Diederik van Binsbergen
CORRESPONDING AUTHOR
Department of Mechanical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
Department of Marine Technology, NTNU, Jonsvannsveien 82, Trondheim, 7050, Norway
Pieter-Jan Daems
Department of Mechanical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
Timothy Verstraeten
Department of Mechanical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
Amir R. Nejad
Department of Marine Technology, NTNU, Jonsvannsveien 82, Trondheim, 7050, Norway
Jan Helsen
Department of Mechanical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
Related authors
No articles found.
Simon Daenens, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 10, 1137–1152, https://doi.org/10.5194/wes-10-1137-2025, https://doi.org/10.5194/wes-10-1137-2025, 2025
Short summary
Short summary
This study presents a novel model for predicting wind turbine power output at a high temporal resolution in wind farms using a hybrid graph neural network (GNN) and long short-term memory (LSTM) architecture. By modeling the wind farm as a graph, the model captures both spatial and temporal dynamics, outperforming traditional power curve methods. Integrated with a normal behavior model (NBM) framework, the model effectively identifies and analyzes power loss events.
Ivo Vervlimmeren, Xavier Chesterman, Timothy Verstraeten, Ann Nowé, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-49, https://doi.org/10.5194/wes-2025-49, 2025
Revised manuscript under review for WES
Short summary
Short summary
We introduce a new method to refine failure prediction for wind turbines, leading to better and more efficient alarming. We do this by filtering detected anomalies based on the anomalies from the whole fleet. We compare submethods and find one that removes up to 65 % of detected anomalies while leaving the failure-predicting ones. We also detail how we trained the model that generated these anomalies and discuss the construction of the scalable pipeline that was used to deploy such models.
Ryota Wada, Amir R. Nejad, Kazuhiro Iijima, Junji Shimazaki, Mihaela Ibrion, Shinnosuke Wanaka, Hideo Nomura, Yoshitaka Mizushima, Takuya Nakashima, and Ken Takagi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-74, https://doi.org/10.5194/wes-2025-74, 2025
Preprint under review for WES
Short summary
Short summary
This paper identifies technology gaps on the path to achieve sustainable development of floating offshore wind in Japan. The key challenges emphasizing on unique conditions such as earthquakes and tropical cyclones are addressed. In Japan the absence of oil & gas has led to social challenges like lack of supply chain, infrastructure, and human resources in offshore wind. Site selection, design & operation, industry development, and contribution to national energy policy "S + 3E" are studied.
Stijn Ally, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 10, 779–812, https://doi.org/10.5194/wes-10-779-2025, https://doi.org/10.5194/wes-10-779-2025, 2025
Short summary
Short summary
Wind farms are crucial for a sustainable energy future. However, their power can fluctuate significantly due to changing weather conditions, which complexly affect their power generation. This paper presents a novel machine-learning-based method to enhance wind farm power predictions, enabling improved power scheduling, trading and grid balancing. This makes wind power more valuable and easier to integrate into the energy system.
Yuksel R. Alkarem, Ian Ammerman, Kimberly Huguenard, Richard W. Kimball, Babak Hejrati, Amrit Verma, Amir R. Nejad, Reza Hashemi, and Stephan Grilli
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-31, https://doi.org/10.5194/wes-2025-31, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Laboratory testing campaigns for wind energy industry plays an essential role in testing innovative control strategies and digital twin applications. But incidents during testing can be detrimental and might cause project delays and damage to expensive equipment. We propose an anomaly detection scheme for laboratory experiments that are developed and tested to enhance reaction time and prediction quality, reducing the likelihood of damage to equipment due to human error or software malfunction.
Konstantinos Vratsinis, Rebeca Marini, Pieter-Jan Daems, Lukas Pauscher, Jeroen van Beeck, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-32, https://doi.org/10.5194/wes-2025-32, 2025
Preprint under review for WES
Short summary
Short summary
Using data collected over 13 months at an offshore wind farm, our study shows that a wind turbine’s position within the farm influences its energy output at a given wind speed. Front-row turbines respond differently to similar wind speeds and turbulence than those further back. This finding suggests that current methods for characterizing inflow conditions may not fully capture actual wind behavior, underscoring the need for improved performance analysis techniques.
Jakob Gebel, Ashkan Rezaei, Adithya Vemuri, Veronica Liverud Krathe, Pieter-Jan Daems, Jens Jo Matthys, Jonathan Sterckx, Konstantinos Vratsinis, Kayacan Kestel, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-173, https://doi.org/10.5194/wes-2024-173, 2025
Preprint under review for WES
Short summary
Short summary
A simulation model of a deployed offshore wind turbine was developed using real-world measurement data. The method shows how to obtain, update and validate a simulation model and allows to improve the efficiency and longevity of offshore wind turbines and support operation and maintenance decisions. Simulations were conducted to analyze the effects of turbulence and wind patterns on turbine lifespan, providing insights to improve maintenance planning and reduce operational costs.
Rebeca Marini, Konstantinos Vratsinis, Kayacan Kestel, Jonathan Sterckx, Jens Matthys, Pieter-Jan Daems, Timothy Verstraeten, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-9, https://doi.org/10.5194/wes-2025-9, 2025
Revised manuscript not accepted
Short summary
Short summary
This work evaluated the wind profile in a Belgian offshore zone. The estimated wind profile was made using measurements that allow for reconstruction at heights along the rotor area. The IEC standard defines these profiles as a 1/7th power law, which is proven not to occur 100 % of the time. It is also possible to infer that there will be differences when using different wind profiles for load assessment, as more realistic profiles can lead to a better assessment of the wind turbine's lifetime.
Felix C. Mehlan and Amir R. Nejad
Wind Energ. Sci., 10, 417–433, https://doi.org/10.5194/wes-10-417-2025, https://doi.org/10.5194/wes-10-417-2025, 2025
Short summary
Short summary
A digital twin is a virtual representation that mirrors the wind turbine's real behavior through simulation models and sensor measurements and can assist in making key decisions such as planning the replacement of parts. These models and measurements are, of course, not perfect and only give an incomplete picture of the real behavior. This study investigates how large the uncertainty of such models and measurements is and to what extent it affects the decision-making process.
Ashkan Rezaei and Amir Rasekhi Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-186, https://doi.org/10.5194/wes-2024-186, 2025
Preprint under review for WES
Short summary
Short summary
We analyzed the reliability of wind turbine blade bearings under extreme turbulence wind conditions to improve their safety and performance. Using simulations, we studied how factors like turbulence and design variations affect failure probability. Our findings show that current standards may underestimate these risks and highlight the need for stricter controls on critical design parameters.
Faras Jamil, Cédric Peeters, Timothy Verstraeten, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-114, https://doi.org/10.5194/wes-2024-114, 2024
Revised manuscript accepted for WES
Short summary
Short summary
A hybrid fault detection method is proposed, which combines physical domain knowledge with machine learning models to automatically detect mechanical faults in wind turbine drivetrain components. It offers detailed insights for experts while giving operators a high-level overview of the machine's health to assist in planning effective maintenance strategies. It was validated on multiple years of wind farm data and the potential faults were accurately predicted, which was confirmed by experts.
Ali Dibaj, Mostafa Valavi, and Amir R. Nejad
Wind Energ. Sci., 9, 2063–2086, https://doi.org/10.5194/wes-9-2063-2024, https://doi.org/10.5194/wes-9-2063-2024, 2024
Short summary
Short summary
This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore methods of early fault detection. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large megawatt-scale offshore wind generators.
Yuksel Rudy Alkarem, Kimberly Huguenard, Richard Kimball, Spencer Hallowell, Amrit Verma, Erin Bachynski-Polić, and Amir Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-67, https://doi.org/10.5194/wes-2024-67, 2024
Preprint withdrawn
Short summary
Short summary
This research is a "less-is-more" demonstration of a novel concept that boost the efficiency of floating wind farms while maintaining fewer number of mooring line/anchors, reducing cost and the large footprint wind farms can have over the ocean bed and the water column. The novelty of this work lies in the passive wake steering method to enhance annual energy production and in integrating that with configurations that allow shared/multiline anchoring potential.
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 8, 893–924, https://doi.org/10.5194/wes-8-893-2023, https://doi.org/10.5194/wes-8-893-2023, 2023
Short summary
Short summary
This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction for wind turbines using SCADA data. The focus lies on techniques that respond to requirements of the industry, e.g., robustness, transparency, computational efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e., generator bearing failures, generator fan failures, and generator stator failures.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
Short summary
Short summary
The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, https://doi.org/10.5194/wes-7-387-2022, 2022
Short summary
Short summary
This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps.
Related subject area
Thematic area: Fluid mechanics | Topic: Wakes and wind farm aerodynamics
Experimental demonstration of regenerative wind farming using a high-density layout of vertical-axis wind turbines
Modeling the effects of active wake mixing on wake behavior through large-scale coherent structures
Wake development in floating wind turbines: new insights and an open dataset from wind tunnel experiments
Spatiotemporal behavior of the far wake of a wind turbine model subjected to harmonic motions: phase averaging applied to stereo particle image velocimetry measurements
Spatial development of planar and axisymmetric wakes of porous objects under a pressure gradient: a wind tunnel study
Numerical investigation of regenerative wind farms featuring enhanced vertical energy entrainment
Convergence and efficiency of global bases using proper orthogonal decomposition for capturing wind turbine wake aerodynamics
Direct integration of non-axisymmetric Gaussian wind-turbine wake including yaw and wind-veer effects
Turbine- and farm-scale power losses in wind farms: an alternative to wake and farm blockage losses
Effect of blockage on wind turbine power and wake development
Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest
Introduction and comparison of novel deep learning and optimization approaches to analytical wake modeling of a tilted wind turbine
Proof of concept for multirotor systems with vortex-generating modes for regenerative wind energy: a study based on numerical simulations and experimental data
Synchronised WindScanner field measurements of the induction zone between two closely spaced wind turbines
Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons
Improvements to the dynamic wake meandering model by incorporating the turbulent Schmidt number
An actuator sector model for wind power applications: a parametric study
Wind tunnel investigations of an individual pitch control strategy for wind farm power optimization
The near-wake development of a wind turbine operating in stalled conditions – Part 1: Assessment of numerical models
Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations
Floating wind turbine motion signature in the far-wake spectral content – a wind tunnel experiment
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 1: Large-eddy-simulation study
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling
Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects
A method to correct for the effect of blockage and wakes on power performance measurements
Vortex model of the aerodynamic wake of airborne wind energy systems
A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling
Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation
The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems
Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model
Actuator line model using simplified force calculation methods
Brief communication: A clarification of wake recovery mechanisms
Predictive and stochastic reduced-order modeling of wind turbine wake dynamics
Wind turbine wake simulation with explicit algebraic Reynolds stress modeling
Including realistic upper atmospheres in a wind-farm gravity-wave model
David Bensason, Jayant Mulay, Andrea Sciacchitano, and Carlos Ferreira
Wind Energ. Sci., 10, 1499–1528, https://doi.org/10.5194/wes-10-1499-2025, https://doi.org/10.5194/wes-10-1499-2025, 2025
Short summary
Short summary
The wake of a scaled vertical-axis wind turbine farm was measured, resulting in the first experimental database of 3D-resolved flow-field measurements. In addition to the baseline operating conditions, two modes of wake control were tested, which involve the passive adjustment of the rotor blade pitch. The results highlight the impacts of these mode adjustments on the trailing vorticity system, wake topology, and affinity towards increasing the rate of wake recovery throughout the farm.
Lawrence Cheung, Gopal Yalla, Prakash Mohan, Alan Hsieh, Kenneth Brown, Nathaniel deVelder, Daniel Houck, Marc T. Henry de Frahan, Marc Day, and Michael Sprague
Wind Energ. Sci., 10, 1403–1420, https://doi.org/10.5194/wes-10-1403-2025, https://doi.org/10.5194/wes-10-1403-2025, 2025
Short summary
Short summary
Mitigating turbine wakes is an important aspect to maximizing wind farm energy production but is a challenge to model. We demonstrate a new approach to modeling active wake mixing, which re-energizes turbine wake through periodic blade pitching. The new model divides the wake into separate steady, unsteady, and turbulent components and solves for each in a computationally efficient manner. Our results show that the model can reasonably predict the faster wake recovery due to mixing.
Alessandro Fontanella, Alberto Fusetti, Stefano Cioni, Francesco Papi, Sara Muggiasca, Giacomo Persico, Vincenzo Dossena, Alessandro Bianchini, and Marco Belloli
Wind Energ. Sci., 10, 1369–1387, https://doi.org/10.5194/wes-10-1369-2025, https://doi.org/10.5194/wes-10-1369-2025, 2025
Short summary
Short summary
This paper investigates the impact of large movements allowed by floating wind turbine foundations on their aerodynamics and wake behavior. Wind tunnel tests with a model turbine reveal that platform motions affect wake patterns and turbulence levels. Insights from these experiments are crucial for optimizing large-scale floating wind farms. The dataset obtained from the experiment is published and can aid in developing simulation tools for floating wind turbines.
Antonin Hubert, Boris Conan, and Sandrine Aubrun
Wind Energ. Sci., 10, 1351–1368, https://doi.org/10.5194/wes-10-1351-2025, https://doi.org/10.5194/wes-10-1351-2025, 2025
Short summary
Short summary
The paper aims to study the far wake of a wind turbine under realistic inflow conditions subjected to harmonic floating motions. The present work shows that phase averaging enables the observation of the coherent spatiotemporal wake behaviour in response to the harmonic motions, contrary to previous studies with time averaging, and that the resulting variations in the chosen metrics exhibit an intensity higher than those expected when using basic quasi-steady-state approaches.
Wessel van der Deijl, Martín Obligado, Stéphane Barre, and Christophe Sicot
Wind Energ. Sci., 10, 719–732, https://doi.org/10.5194/wes-10-719-2025, https://doi.org/10.5194/wes-10-719-2025, 2025
Short summary
Short summary
We present a wind tunnel study on the effect of an adverse pressure gradient on wakes from porous discs and cylinders. We quantified the spatial development of turbulent wakes for Reynolds numbers up to 3.9 × 105 and at distances ranging from 1 to 12 diameters downstream, both with and without an adverse pressure gradient. Consistent with previous studies, we find that the pressure gradient has an effect in all cases, resulting in larger velocity deficits and wider wakes.
YuanTso Li, Wei Yu, Andrea Sciacchitano, and Carlos Ferreira
Wind Energ. Sci., 10, 631–659, https://doi.org/10.5194/wes-10-631-2025, https://doi.org/10.5194/wes-10-631-2025, 2025
Short summary
Short summary
A novel wind farm concept, called a regenerative wind farm, is investigated numerically. This concept tackles the significant wake interaction losses among traditional wind farms. Our results show that regenerative wind farms can greatly reduce these losses, boosting power output per unit surface. Unlike traditional farms with three-bladed wind turbines, regenerative farms use multi-rotor systems with lifting devices (MRSLs). This unconventional design effectively reduces wake losses.
Juan Felipe Céspedes Moreno, Juan Pablo Murcia León, and Søren Juhl Andersen
Wind Energ. Sci., 10, 597–611, https://doi.org/10.5194/wes-10-597-2025, https://doi.org/10.5194/wes-10-597-2025, 2025
Short summary
Short summary
Using a global base in a proper orthogonal decomposition provides a common base for analyzing flows, such as wind turbine wakes, across an entire parameter space. This can be used to compare flows with different conditions using the same physical interpretation. This work shows the convergence of the global base, its small error compared to the truncation error in the flow reconstruction, and the insensitivity to which datasets are included for generating the global base.
Karim Ali, Pablo Ouro, and Tim Stallard
Wind Energ. Sci., 10, 511–533, https://doi.org/10.5194/wes-10-511-2025, https://doi.org/10.5194/wes-10-511-2025, 2025
Short summary
Short summary
We introduce an innovative analytical method to better understand and optimise wind-farm performance by accurately calculating how turbine wakes affect each other. Unlike traditional numerical approaches, our method provides a precise way to measure the impact of upstream wakes on downstream turbines. This new approach, validated through numerical comparisons, enhances optimisation strategies, potentially leading to more efficient wind-farm operations and increased power generation.
Andrew Kirby, Takafumi Nishino, Luca Lanzilao, Thomas D. Dunstan, and Johan Meyers
Wind Energ. Sci., 10, 435–450, https://doi.org/10.5194/wes-10-435-2025, https://doi.org/10.5194/wes-10-435-2025, 2025
Short summary
Short summary
Traditionally, the aerodynamic loss of wind farm efficiency is classified into wake loss and farm blockage loss. This study, using high-fidelity simulations, shows that neither of these two losses is well correlated with the overall farm efficiency. We propose new measures called turbine-scale efficiency and farm-scale efficiency to better describe turbine–wake effects and farm–atmosphere interactions. This study suggests the importance of better modelling farm-scale loss in future studies.
Olivier Ndindayino, Augustin Puel, and Johan Meyers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-6, https://doi.org/10.5194/wes-2025-6, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Our aim is to understand the relationship between flow blockage and improved wind farm efficiency using large-eddy simulations, as well as developing an analytical model that shows promise for improving turbine power predictions under blockage. We found that blockage enhances turbine power and thrust by inducing a favourable pressure drop across the turbine row, while simultaneously inducing an unfavourable pressure increase downstream which has minimal direct impact on far wake development.
Kenneth Brown, Gopal Yalla, Lawrence Cheung, Joeri Frederik, Dan Houck, Nate deVelder, Eric Simley, and Paul Fleming
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-191, https://doi.org/10.5194/wes-2024-191, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This paper presents a one half of a companion-paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for an offshore wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.
James Cutler, Christopher Bay, and Andrew Ning
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-172, https://doi.org/10.5194/wes-2024-172, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This study compares two methods for modeling wakes from tilted wind turbines. An optimized analytical model improves accuracy but is limited by assumptions about wake shape. In contrast, a deep learning approach captures complex wake patterns without these constraints, matching high-fidelity simulations with minimal computational effort. The comparison highlights the potential of deep learning to transform wake modeling, offering greater accuracy and efficiency for wind energy optimization.
Flavio Avila Correia Martins, Alexander van Zuijlen, and Carlos Simão Ferreira
Wind Energ. Sci., 10, 41–58, https://doi.org/10.5194/wes-10-41-2025, https://doi.org/10.5194/wes-10-41-2025, 2025
Short summary
Short summary
This study examines regenerative wind farming with multirotor systems fitted with atmospheric boundary layer control (ABL-control) wings near the rotor's wake. These wings create vortices that boost vertical momentum transfer and speed up wake recovery. Results show that ABL-control wings can restore 95 % of wind power within six rotor diameters downstream, achieving a recovery rate nearly 10 times faster than that without ABL control.
Anantha Padmanabhan Kidambi Sekar, Paul Hulsman, Marijn Floris van Dooren, and Martin Kühn
Wind Energ. Sci., 9, 1483–1505, https://doi.org/10.5194/wes-9-1483-2024, https://doi.org/10.5194/wes-9-1483-2024, 2024
Short summary
Short summary
We present induction zone measurements conducted with two synchronised lidars at a two-turbine wind farm. The induction zone flow was characterised for free, fully waked and partially waked flows. Due to the short turbine spacing, the lidars captured the interaction of the atmospheric boundary layer, induction zone and wake, evidenced by induction asymmetry and induction zone–wake interactions. The measurements will aid the process of further improving existing inflow and wake models.
Kelsey Shaler, Eliot Quon, Hristo Ivanov, and Jason Jonkman
Wind Energ. Sci., 9, 1451–1463, https://doi.org/10.5194/wes-9-1451-2024, https://doi.org/10.5194/wes-9-1451-2024, 2024
Short summary
Short summary
This paper presents a three-way verification and validation between an engineering-fidelity model, a high-fidelity model, and measured data for the wind farm structural response and wake dynamics during an evolving stable boundary layer of a small wind farm, generally with good agreement.
Peter Brugger, Corey D. Markfort, and Fernando Porté-Agel
Wind Energ. Sci., 9, 1363–1379, https://doi.org/10.5194/wes-9-1363-2024, https://doi.org/10.5194/wes-9-1363-2024, 2024
Short summary
Short summary
The dynamic wake meandering model (DWMM) assumes that wind turbine wakes are transported like a passive tracer by the large-scale turbulence of the atmospheric boundary layer. We show that both the downstream transport and the lateral transport of the wake have differences from the passive tracer assumption. We then propose to include the turbulent Schmidt number into the DWMM to account for the less efficient transport of momentum and show that it improves the quality of the model predictions.
Mohammad Mehdi Mohammadi, Hugo Olivares-Espinosa, Gonzalo Pablo Navarro Diaz, and Stefan Ivanell
Wind Energ. Sci., 9, 1305–1321, https://doi.org/10.5194/wes-9-1305-2024, https://doi.org/10.5194/wes-9-1305-2024, 2024
Short summary
Short summary
This paper has put forward a set of recommendations regarding the actuator sector model implementation details to improve the capability of the model to reproduce similar results compared to those obtained by an actuator line model, which is one of the most common ways used for numerical simulations of wind farms, while providing significant computational savings. This includes among others the velocity sampling method and a correction of the sampled velocities to calculate the blade forces.
Franz V. Mühle, Florian M. Heckmeier, Filippo Campagnolo, and Christian Breitsamter
Wind Energ. Sci., 9, 1251–1271, https://doi.org/10.5194/wes-9-1251-2024, https://doi.org/10.5194/wes-9-1251-2024, 2024
Short summary
Short summary
Wind turbines influence each other, and these wake effects limit the power production of downstream turbines. Controlling turbines collectively and not individually can limit such effects. We experimentally investigate a control strategy increasing mixing in the wake. We want to see the potential of this so-called Helix control for power optimization and understand the flow physics. Our study shows that the control technique leads to clearly faster wake recovery and thus higher power production.
Pascal Weihing, Marion Cormier, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 9, 933–962, https://doi.org/10.5194/wes-9-933-2024, https://doi.org/10.5194/wes-9-933-2024, 2024
Short summary
Short summary
This study evaluates different approaches to simulate the near-wake flow of a wind turbine. The test case is in off-design conditions of the wind turbine, where the flow is separated from the blades and therefore very difficult to predict. The evaluation of simulation techniques is key to understand their limitations and to deepen the understanding of the near-wake physics. This knowledge can help to derive new wind farm design methods for yield-optimized farm layouts.
Nikolaos Bempedelis, Filippo Gori, Andrew Wynn, Sylvain Laizet, and Luca Magri
Wind Energ. Sci., 9, 869–882, https://doi.org/10.5194/wes-9-869-2024, https://doi.org/10.5194/wes-9-869-2024, 2024
Short summary
Short summary
This paper proposes a computational method to maximise the power production of wind farms through two strategies: layout optimisation and yaw angle optimisation. The proposed method relies on high-fidelity computational modelling of wind farm flows and is shown to be able to effectively maximise wind farm power production. Performance improvements relative to conventional optimisation strategies based on low-fidelity models can be attained, particularly in scenarios of increased flow complexity.
Benyamin Schliffke, Boris Conan, and Sandrine Aubrun
Wind Energ. Sci., 9, 519–532, https://doi.org/10.5194/wes-9-519-2024, https://doi.org/10.5194/wes-9-519-2024, 2024
Short summary
Short summary
This paper studies the consequences of floater motions for the wake properties of a floating wind turbine. Since wake interactions are responsible for power production loss in wind farms, it is important that we know whether the tools that are used to predict this production loss need to be upgraded to take into account these aspects. Our wind tunnel study shows that the signature of harmonic floating motions can be observed in the far wake of a wind turbine, when motions have strong amplitudes.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 97–117, https://doi.org/10.5194/wes-9-97-2024, https://doi.org/10.5194/wes-9-97-2024, 2024
Short summary
Short summary
Wind turbine wakes affect the production and lifecycle of downstream turbines. They can be predicted with the dynamic wake meandering (DWM) method. In this paper, the authors break down the velocity and turbulence in the wake of a wind turbine into several terms. They show that it is implicitly assumed in the DWM that some of these terms are neglected. With high-fidelity simulations, it is shown that this can lead to some errors, in particular for the maximum turbulence added by the wake.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 119–139, https://doi.org/10.5194/wes-9-119-2024, https://doi.org/10.5194/wes-9-119-2024, 2024
Short summary
Short summary
Analytical models allow us to quickly compute the decreased power output and lifetime induced by wakes in a wind farm. This is achieved by evaluating the modified velocity and turbulence in the wake. In this work, we present a new model based on the velocity and turbulence breakdowns presented in Part 1. This new model is physically based, allows us to compute the whole turbulence profile (rather than the maximum value) and is built to take atmospheric stability into account.
Maarten J. van den Broek, Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, https://doi.org/10.5194/wes-8-1909-2023, 2023
Short summary
Short summary
As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
Alessandro Sebastiani, James Bleeg, and Alfredo Peña
Wind Energ. Sci., 8, 1795–1808, https://doi.org/10.5194/wes-8-1795-2023, https://doi.org/10.5194/wes-8-1795-2023, 2023
Short summary
Short summary
The power curve of a wind turbine indicates the turbine power output in relation to the wind speed. Therefore, power curves are critically important to estimate the production of future wind farms as well as to assess whether operating wind farms are functioning correctly. Since power curves are often measured in wind farms, they might be affected by the interactions between the turbines. We show that these effects are not negligible and present a method to correct for them.
Filippo Trevisi, Carlo E. D. Riboldi, and Alessandro Croce
Wind Energ. Sci., 8, 999–1016, https://doi.org/10.5194/wes-8-999-2023, https://doi.org/10.5194/wes-8-999-2023, 2023
Short summary
Short summary
Modeling the aerodynamic wake of airborne wind energy systems (AWESs) is crucial to properly estimating power production and to designing such systems. The velocities induced at the AWES from its own wake are studied with a model for the near wake and one for the far wake, using vortex methods. The model is validated with the lifting-line free-vortex wake method implemented in QBlade.
Maarten Paul van der Laan, Oscar García-Santiago, Mark Kelly, Alexander Meyer Forsting, Camille Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, Alfredo Peña, Niels Nørmark Sørensen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, https://doi.org/10.5194/wes-8-819-2023, 2023
Short summary
Short summary
Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher-fidelity numerical models and validated with measurements of an existing wind farm cluster.
Peter Baas, Remco Verzijlbergh, Pim van Dorp, and Harm Jonker
Wind Energ. Sci., 8, 787–805, https://doi.org/10.5194/wes-8-787-2023, https://doi.org/10.5194/wes-8-787-2023, 2023
Short summary
Short summary
This work studies the energy production and wake losses of large offshore wind farms with a large-eddy simulation model. Therefore, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW wind farm scenarios. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Also, a clear impact of atmospheric stability on the energy production is found.
Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso
Wind Energ. Sci., 8, 691–723, https://doi.org/10.5194/wes-8-691-2023, https://doi.org/10.5194/wes-8-691-2023, 2023
Short summary
Short summary
The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.
Tamino Wetz and Norman Wildmann
Wind Energ. Sci., 8, 515–534, https://doi.org/10.5194/wes-8-515-2023, https://doi.org/10.5194/wes-8-515-2023, 2023
Short summary
Short summary
In the present study, for the first time, the SWUF-3D fleet of multirotors is deployed for field measurements on an operating 2 MW wind turbine (WT) in complex terrain. The fleet of multirotors has the potential to fill the meteorological gap of observations in the near wake of WTs with high-temporal and high-spatial-resolution wind vector measurements plus temperature, humidity and pressure. The flow up- and downstream of the WT is measured simultaneously at multiple spatial positions.
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.
Gonzalo Pablo Navarro Diaz, Alejandro Daniel Otero, Henrik Asmuth, Jens Nørkær Sørensen, and Stefan Ivanell
Wind Energ. Sci., 8, 363–382, https://doi.org/10.5194/wes-8-363-2023, https://doi.org/10.5194/wes-8-363-2023, 2023
Short summary
Short summary
In this paper, the capacity to simulate transient wind turbine wake interaction problems using limited wind turbine data has been extended. The key novelty is the creation of two new variants of the actuator line technique in which the rotor blade forces are computed locally using generic load data. The analysis covers a partial wake interaction case between two wind turbines for a uniform laminar inflow and for a turbulent neutral atmospheric boundary layer inflow.
Maarten Paul van der Laan, Mads Baungaard, and Mark Kelly
Wind Energ. Sci., 8, 247–254, https://doi.org/10.5194/wes-8-247-2023, https://doi.org/10.5194/wes-8-247-2023, 2023
Short summary
Short summary
Understanding wind turbine wake recovery is important to mitigate energy losses in wind farms. Wake recovery is often assumed or explained to be dependent on the first-order derivative of velocity. In this work we show that wind turbine wakes recover mainly due to the second-order derivative of the velocity, which transport momentum from the freestream towards the wake center. The wake recovery mechanisms and results of a high-fidelity numerical simulation are illustrated using a simple model.
Søren Juhl Andersen and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 2117–2133, https://doi.org/10.5194/wes-7-2117-2022, https://doi.org/10.5194/wes-7-2117-2022, 2022
Short summary
Short summary
Simulating the turbulent flow inside large wind farms is inherently complex and computationally expensive. A new and fast model is developed based on data from high-fidelity simulations. The model captures the flow dynamics with correct statistics for a wide range of flow conditions. The model framework provides physical insights and presents a generalization of high-fidelity simulation results beyond the case-specific scenarios, which has significant potential for future turbulence modeling.
Mads Baungaard, Stefan Wallin, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci., 7, 1975–2002, https://doi.org/10.5194/wes-7-1975-2022, https://doi.org/10.5194/wes-7-1975-2022, 2022
Short summary
Short summary
Wind turbine wakes in the neutral atmospheric surface layer are simulated with Reynolds-averaged Navier–Stokes (RANS) using an explicit algebraic Reynolds stress model. Contrary to standard two-equation turbulence models, it can predict turbulence anisotropy and complex physical phenomena like secondary motions. For the cases considered, it improves Reynolds stress, turbulence intensity, and velocity deficit predictions, although a more top-hat-shaped profile is observed for the latter.
Koen Devesse, Luca Lanzilao, Sebastiaan Jamaer, Nicole van Lipzig, and Johan Meyers
Wind Energ. Sci., 7, 1367–1382, https://doi.org/10.5194/wes-7-1367-2022, https://doi.org/10.5194/wes-7-1367-2022, 2022
Short summary
Short summary
Recent research suggests that offshore wind farms might form such a large obstacle to the wind that it already decelerates before reaching the first turbines. Part of this phenomenon could be explained by gravity waves. Research on these gravity waves triggered by mountains and hills has found that variations in the atmospheric state with altitude can have a large effect on how they behave. This paper is the first to take the impact of those vertical variations into account for wind farms.
Cited articles
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: Optuna: A Next-generation Hyperparameter Optimization Framework, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Alaska, USA, 4–8 August 2019, https://doi.org/10.1145/3292500.3330701, 2019. a, b
Allaerts, D. and Meyers, J.: Gravity Waves and Wind-Farm Efficiency in Neutral and Stable Conditions, Bound.-Lay. Meteorol., 166, 269–299, https://doi.org/10.1007/s10546-017-0307-5, 2017. a
Annoni, J., Gebraad, P. M. O., Scholbrock, A. K., Fleming, P. A., and van Wingerden, J.-W.: Analysis of axial-induction-based wind plant control using an engineering and a high-order wind plant model, Wind Energy, 19, 1135–1150, https://doi.org/10.1002/we.1891, 2015. a
Archer, C. L., Vasel-Be-Hagh, A., Yan, C., Wu, S., Pan, Y., Brodie, J. F., and Maguire, A. E.: Review and evaluation of wake loss models for wind energy applications, Appl. Energy, 226, 1187–1207, https://doi.org/10.1016/j.apenergy.2018.05.085, 2018. a
Ávila, F. J., Verstraeten, T., Vratsinis, K., Nowé, A., and Helsen, J.: Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs, J. Phys.: Conf. Ser., 2505, 012035, https://doi.org/10.1088/1742-6596/2505/1/012035, 2023. a
Baker, N. F., Stanley, A. P., Thomas, J. J., Ning, A., and Dykes, K.: Best Practices for Wake Model and Optimization Algorithm Selection in Wind Farm Layout Optimization, in: AIAA Scitech 2019 Forum, American Institute of Aeronautics and Astronautics, https://doi.org/10.2514/6.2019-0540, 2019. a
Barthelmie, R., Badger, J., Pryor, S., Hasager, C., Christiansen, M., and Jørgensen, B.: Offshore Coastal Wind Speed Gradients: Issues for the Design and Development of Large Offshore Windfarms, Wind Eng., 31, 369–382, https://doi.org/10.1260/030952407784079762, 2007. a
Barthelmie, R. J., Hansen, K., Frandsen, S. T., Rathmann, O., Schepers, J. G., Schlez, W., Phillips, J., Rados, K., Zervos, A., Politis, E. S., and Chaviaropoulos, P. K.: Modelling and measuring flow and wind turbine wakes in large wind farms offshore, Wind Energy, 12, 431–444, https://doi.org/10.1002/we.348, 2009. a, b
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energy, 70, 116–123, https://doi.org/10.1016/j.renene.2014.01.002, 2014. a, b, c, d
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541, https://doi.org/10.1017/jfm.2016.595, 2016. a, b, c
Bastankhah, M., Welch, B. L., Martínez-Tossas, L. A., King, J., and Fleming, P.: Analytical solution for the cumulative wake of wind turbines in wind farms, J. Fluid Mech., 911, A53, https://doi.org/10.1017/jfm.2020.1037, 2021. a, b
Bay, C. J., Fleming, P., Doekemeijer, B., King, J., Churchfield, M., and Mudafort, R.: Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model, Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, 2023. a, b, c
Becker, M., Ritter, B., Doekemeijer, B., van der Hoek, D., Konigorski, U., Allaerts, D., and van Wingerden, J.-W.: The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake, Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, 2022. a
Bergstra, J. and Bengio, Y.: Random Search for Hyper-Parameter Optimization, J. Mach. Learn. Res., 13, 281–305, 2012. a
Bergstra, J., Yamins, D., and Cox, D. D.: Making a Science of Model Search, arXiv [preprint], https://doi.org/10.48550/ARXIV.1209.5111, 2012. a
Bleeg, J., Purcell, M., Ruisi, R., and Traiger, E.: Wind Farm Blockage and the Consequences of Neglecting Its Impact on Energy Production, Energies, 11, 1609, https://doi.org/10.3390/en11061609, 2018. a
Blondel, F. and Cathelain, M.: An alternative form of the super-Gaussian wind turbine wake model, Wind Energ. Sci., 5, 1225–1236, https://doi.org/10.5194/wes-5-1225-2020, 2020. a, b, c
Bossanyi, E. and Ruisi, R.: Axial induction controller field test at Sedini wind farm, Wind Energ. Sci., 6, 389–408, https://doi.org/10.5194/wes-6-389-2021, 2021. a
Branlard, E. and Forsting, A. R. M.: Assessing the blockage effect of wind turbines and wind farms using an analytical vortex model, Wind Energy, 23, 2068–2086, https://doi.org/10.1002/we.2546, 2020. a
Branlard, E., Quon, E., Forsting, A. R. M., King, J., and Moriarty, P.: Wind farm blockage effects: comparison of different engineering models, J. Phys.: Conf. Ser., 1618, 062036, https://doi.org/10.1088/1742-6596/1618/6/062036, 2020. a
Campagnolo, F., Imširović, L., Braunbehrens, R., and Bottasso, C. L.: Further calibration and validation of FLORIS with wind tunnel data, J. Phys.: Conf. Ser., 2265, 022019, https://doi.org/10.1088/1742-6596/2265/2/022019, 2022. a
Cañadillas, B., Beckenbauer, M., Trujillo, J. J., Dörenkämper, M., Foreman, R., Neumann, T., and Lampert, A.: Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling, Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, 2022. a
Crespo, A. and Hernandez, J.: Turbulence characteristics in wind-turbine wakes, J. Wind Eng. Indust. Aerodynam., 61, 71–85, https://doi.org/10.1016/0167-6105(95)00033-x, 1996. a
Daems, P.-J., Verstraeten, T., Peeters, C., and Helsen, J.: Effects of wake on gearbox design load cases identified from fleet-wide operational data, Forsch. Ingenieurwes., 85, 553–558, https://doi.org/10.1007/s10010-021-00444-3, 2021. a
Daems, P.-J., Peeters, C., Matthys, J., Verstraeten, T., and Helsen, J.: Fleet-wide analytics on field data targeting condition and lifetime aspects of wind turbine drivetrains, Forsch. Ingenieurwes., 87, 285–295, https://doi.org/10.1007/s10010-023-00643-0, 2023. a
Dilip, D. and Porté-Agel, F.: Wind Turbine Wake Mitigation through Blade Pitch Offset, Energies, 10, 757, https://doi.org/10.3390/en10060757, 2017. a
Doekemeijer, B. M., Wingerden, J.-W. V., and Fleming, P. A.: A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model, in: IEEE 2019 American Control Conference (ACC), 10–12 July 2019, Philadelphia, PA, USA, https://doi.org/10.23919/acc.2019.8815126, 2019. 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, https://doi.org/10.1016/j.renene.2020.04.007, 2020. a, b, c
Doekemeijer, B. M., Kern, S., Maturu, S., Kanev, S., Salbert, B., Schreiber, J., Campagnolo, F., Bottasso, C. L., Schuler, S., Wilts, F., Neumann, T., Potenza, G., Calabretta, F., Fioretti, F., and van Wingerden, J.-W.: Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy, Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, 2021. a, b
DTU: PyWake, GitHub [code], https://github.com/DTUWindEnergy/PyWake (last access: 26 June 2024), 2023. 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., Annoni, J., Churchfield, M., Martinez-Tossas, L. A., Gruchalla, K., Lawson, M., and Moriarty, P.: A simulation study demonstrating the importance of large-scale trailing vortices in wake steering, Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, 2018. 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, b
Fleming, P., King, J., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Jager, D., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2, Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, 2020. a
Fleming, P., Sinner, M., Young, T., Lannic, M., King, J., Simley, E., and Doekemeijer, B.: Experimental results of wake steering using fixed angles, Wind Energ. Sci., 6, 1521–1531, https://doi.org/10.5194/wes-6-1521-2021, 2021. a
Fuertes, F. C., Markfort, C., and Porté-Agel, F.: Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation, Remote Sens., 10, 668, https://doi.org/10.3390/rs10050668, 2018. a
Gebraad, P. M. O., Teeuwisse, F. W., van Wingerden, J. W., Fleming, P. A., Ruben, S. D., Marden, J. R., and Pao, L. Y.: Wind plant power optimization through yaw control using a parametric model for wake effects-a CFD simulation study, Wind Energy, 19, 95–114, https://doi.org/10.1002/we.1822, 2014. a
Gebraad, P. M. O., Churchfield, M. J., and Fleming, P. A.: Incorporating Atmospheric Stability Effects into the FLORIS Engineering Model of Wakes in Wind Farms, J. Phys.: Conf. Ser., 753, 052004, https://doi.org/10.1088/1742-6596/753/5/052004, 2016. a, b
Göçmen, T. and Giebel, G.: Data-driven Wake Modelling for Reduced Uncertainties in short-term Possible Power Estimation, J. Phys.: Conf. Ser., 1037, 072002, https://doi.org/10.1088/1742-6596/1037/7/072002, 2018. a
Göçmen, T., Campagnolo, F., Duc, T., Eguinoa, I., Andersen, S. J., Petrović, V., Imširović, L., Braunbehrens, R., Liew, J., Baungaard, M., van der Laan, M. P., Qian, G., Aparicio-Sanchez, M., González-Lope, R., Dighe, V. V., Becker, M., van den Broek, M. J., van Wingerden, J.-W., Stock, A., Cole, M., Ruisi, R., Bossanyi, E., Requate, N., Strnad, S., Schmidt, J., Vollmer, L., Sood, I., and Meyers, J.: FarmConners wind farm flow control benchmark – Part 1: Blind test results, Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, 2022. a, b, c, d, e, f, g
Hamilton, N., Bay, C. J., Fleming, P., King, J., and Martínez-Tossas, L. A.: Comparison of modular analytical wake models to the Lillgrund wind plant, J. Renew. Sustain. Energ., 12, 053311, https://doi.org/10.1063/5.0018695, 2020. a
Hansen, K. S., Barthelmie, R. J., Jensen, L. E., and Sommer, A.: The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm, Wind Energy, 15, 183–196, https://doi.org/10.1002/we.512, 2011. a
IEA: Wind Electricity, licence: CC BY 4.0, https://www.iea.org/reports/wind-electricity (last access: 29 June 2023), 2022. a
Jensen, N.: A note on wind generator interaction, Rep. RISØ-M-2411, https://orbit.dtu.dk/en/publications/a-note-on-wind-generator-interaction (last access: 26 June 2024), 1983. a
Katić, I., Højstrup, J., and Jensen, N.: A Simple Model for Cluster Efficiency, European wind energy association conference and exhibition, Rome, Italy, 6–8 October 1986, Vol. 1, 407–410, https://orbit.dtu.dk/en/publications/a-simple-model-for-cluster-efficiency (last access: 26 June 2024), 1987. a, b
Kheirabadi, A. C. and Nagamune, R.: A quantitative review of wind farm control with the objective of wind farm power maximization, J. Wind Eng. Indust. Aerodynam., 192, 45–73, https://doi.org/10.1016/j.jweia.2019.06.015, 2019. a, b
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, b, c, d
Lissaman, P. B. S.: Energy Effectiveness of Arbitrary Arrays of Wind Turbines, J. Energy, 3, 323–328, https://doi.org/10.2514/3.62441, 1979. a
Martínez-Tossas, L. A., Annoni, J., Fleming, P. A., and Churchfield, M. J.: The aerodynamics of the curled wake: a simplified model in view of flow control, Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, 2019. a
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, 2022. a, b
Mudafort, R., Fleming P., Bay, C., Hammond, R., Simley, E., Hamilton, N., Bachant, P., Fleming, K., Doekemeijer, B., Annoni, J., Quon, E., Stanley, P., Sinner, M., Ireland, P. J., Bensason, D., Schreiber, J., Quick, J., Seim, K. S., Benito Cia, P., Sortland, S., Martinez, T., Farrell, A., Duffy, P., and Zerweck, W.: NREL/FLORIS Version 3.4, Zenodo [code], https://doi.org/10.5281/zenodo.7942258, 2023.
Munters, W., Adiloglu, B., Buckingham, S., and van Beeck, J.: Wake impact of constructing a new offshore wind farm zone on an existing downwind cluster: a case study of the Belgian Princess Elisabeth zone using FLORIS, J. Phys.: Conf. Ser., 2265, 022049, https://doi.org/10.1088/1742-6596/2265/2/022049, 2022. a
Nejad, A. R., Keller, J., Guo, Y., Sheng, S., Polinder, H., Watson, S., Dong, J., Qin, Z., Ebrahimi, A., Schelenz, R., Guzmán, F. G., Cornel, D., Golafshan, R., Jacobs, G., Blockmans, B., Bosmans, J., Pluymers, B., Carroll, J., Koukoura, S., Hart, E., McDonald, A., Natarajan, A., Torsvik, J., Moghadam, F. K., Daems, P.-J., Verstraeten, T., Peeters, C., and Helsen, J.: Wind turbine drivetrains: state-of-the-art technologies and future development trends, Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, 2022. a
Niayifar, A. and Porté-Agel, F.: A new analytical model for wind farm power prediction, J. Phys.: Conf. Ser., 625, 012039, https://doi.org/10.1088/1742-6596/625/1/012039, 2015. a, b, c
Niayifar, A. and Porté-Agel, F.: Analytical Modeling of Wind Farms: A New Approach for Power Prediction, Energies, 9, 741, https://doi.org/10.3390/en9090741, 2016. a, b
Nygaard, N. G., Steen, S. T., Poulsen, L., and Pedersen, J. G.: Modelling cluster wakes and wind farm blockage, J. Phys.: Conf. Ser., 1618, 062072, https://doi.org/10.1088/1742-6596/1618/6/062072, 2020. a, b, c, d
Nygaard, N. G., Poulsen, L., Svensson, E., and Pedersen, J. G.: Large-scale benchmarking of wake models for offshore wind farms, J. Phys.: Conf. Ser., 2265, 022008, https://doi.org/10.1088/1742-6596/2265/2/022008, 2022. a, b, c
Optuna: Optuna, Version 3.4.0, GitHub [code], https://github.com/optuna/optuna (last access: 26 June 2024), 2019.
Pedersen, J. G., Svensson, E., Poulsen, L., and Nygaard, N. G.: Turbulence Optimized Park model with Gaussian wake profile, J. Phys.: Conf. Ser., 2265, 022063, https://doi.org/10.1088/1742-6596/2265/2/022063, 2022. a, b, c
Pettas, V., Kretschmer, M., Clifton, A., and Cheng, P. W.: On the effects of inter-farm interactions at the offshore wind farm Alpha Ventus, Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, 2021. a
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-Turbine and Wind-Farm Flows: A Review, Bound.-Lay. Meteorol., 174, 1–59, https://doi.org/10.1007/s10546-019-00473-0, 2019. a, b, c
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
Saltelli, A.: Making best use of model evaluations to compute sensitivity indices, Comput. Phys. Commun., 145, 280–297, https://doi.org/10.1016/s0010-4655(02)00280-1, 2002. a
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S.: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Comput. Phys. Commun., 181, 259–270, https://doi.org/10.1016/j.cpc.2009.09.018, 2010. a
Sanderse, B.: Aerodynamics of wind turbine wakes, Tech. rep., TNO, https://www.osti.gov/etdeweb/biblio/21162007 (last access: 26 June 2024), 2009. a
Sanderse, B., Dighe, V. V., Boorsma, K., and Schepers, G.: Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling, Wind Energ. Sci., 7, 759–781, https://doi.org/10.5194/wes-7-759-2022, 2022. a
Schneemann, J., Theuer, F., Rott, A., Dörenkämper, M., and Kühn, M.: Offshore wind farm global blockage measured with scanning lidar, Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, 2021. a
Schreiber, J., Bottasso, C. L., Salbert, B., and Campagnolo, F.: Improving wind farm flow models by learning from operational data, Wind Energ. Sci., 5, 647–673, https://doi.org/10.5194/wes-5-647-2020, 2020. a, b, c, d
Sickler, M., Ummels, B., Zaaijer, M., Schmehl, R., and Dykes, K.: Offshore wind farm optimisation: a comparison of performance between regular and irregular wind turbine layouts, Wind Energ. Sci., 8, 1225–1233, https://doi.org/10.5194/wes-8-1225-2023, 2023. 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
Sobol, I.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simul., 55, 271–280, https://doi.org/10.1016/s0378-4754(00)00270-6, 2001. a, b
Teng, J. and Markfort, C. D.: A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data, Energies, 13, 3537, https://doi.org/10.3390/en13143537, 2020. a
Thomsen, K. and Sørensen, P.: Fatigue loads for wind turbines operating in wakes, J. Wind Eng. Indust. Aerodynam., 80, 121–136, https://doi.org/10.1016/s0167-6105(98)00194-9, 1999. a
Trabucchi, D., Trujillo, J.-J., and Kühn, M.: Nacelle-based Lidar Measurements for the Calibration of a Wake Model at Different Offshore Operating Conditions, Energy Proced., 137, 77–88, https://doi.org/10.1016/j.egypro.2017.10.335, 2017. a, b
van Binsbergen, D. W., Wang, S., and Nejad, A. R.: Effects of induction and wake steering control on power and drivetrain responses for 10 MW floating wind turbines in a wind farm, J. Phys.: Conf. Ser., 1618, 022044, https://doi.org/10.1088/1742-6596/1618/2/022044, 2020. a
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O., Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki, V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P., Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J., Robertson, A., Rodrigo, J. S., Sempreviva, A. M., Smith, J. C., Tuohy, A., and Wiser, R.: Grand challenges in the science of wind energy, Science, 366, 6464, https://doi.org/10.1126/science.aau2027, 2019. a, b
Veers, P., Bottasso, C. L., Manuel, L., Naughton, J., Pao, L., Paquette, J., Robertson, A., Robinson, M., Ananthan, S., Barlas, T., Bianchini, A., Bredmose, H., Horcas, S. G., Keller, J., Madsen, H. A., Manwell, J., Moriarty, P., Nolet, S., and Rinker, J.: Grand challenges in the design, manufacture, and operation of future wind turbine systems, Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, 2023. a, b
Verstraeten, T., Nowé, A., Keller, J., Guo, Y., Sheng, S., and Helsen, J.: Fleetwide data-enabled reliability improvement of wind turbines, Renew. Sustain. Energ. Rev., 109, 428–437, https://doi.org/10.1016/j.rser.2019.03.019, 2019. a
Verstraeten, T., Daems, P.-J., Bargiacchi, E., Roijers, D. M., Libin, P. J., and Helsen, J.: Scalable Optimization for Wind Farm Control using Coordination Graphs, in: Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), online, 3–7 May 2021, 1362–1370, https://dl.acm.org/doi/10.5555/3463952.3464109 (last access: 26 June 2024), 2021. a
Wang, C., Wang, J., Campagnolo, F., Carraón, D. B., and Bottasso, C. L.: Validation of large-eddy simulation of scaled waked wind turbines in different yaw misalignment conditions, J. Phys.: Conf. Ser., 1037, 062007, https://doi.org/10.1088/1742-6596/1037/6/062007, 2018. a
Wang, Q., Luo, K., Wu, C., Mu, Y., Tan, J., and Fan, J.: Diurnal impact of atmospheric stability on inter-farm wake and power generation efficiency at neighboring onshore wind farms in complex terrain, Energ. Convers. Manage., 267, 115897, https://doi.org/10.1016/j.enconman.2022.115897, 2022. a
Wu, K. and Porté-Agel, F.: Flow Adjustment Inside and Around Large Finite-Size Wind Farms, Energies, 10, 2164, https://doi.org/10.3390/en10122164, 2017. a
Zhang, J. and Zhao, X.: Quantification of parameter uncertainty in wind farm wake modeling, Energy, 196, 117065, https://doi.org/10.1016/j.energy.2020.117065, 2020. a
Zong, H. and Porté-Agel, F.: A momentum-conserving wake superposition method for wind farm power prediction, J. Fluid Mech., 889, A8, https://doi.org/10.1017/jfm.2020.77, 2020. a
Short summary
Wind farm yield assessment often relies on analytical wake models. Calibrating these models can be challenging due to the stochastic nature of wind. We developed a calibration framework that performs a multi-phase optimization on the tuning parameters using time series SCADA data. This yields a parameter distribution that more accurately reflects reality than a single value. Results revealed notable variation in resultant parameter values, influenced by nearby wind farms and coastal effects.
Wind farm yield assessment often relies on analytical wake models. Calibrating these models can...
Altmetrics
Final-revised paper
Preprint