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Wind Energy Science
The interactive open-access journal of the European Academy of Wind Energy
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Abstracted/indexed
WES | Articles | Volume 4, issue 1
Wind Energ. Sci., 4, 139–161, 2019
https://doi.org/10.5194/wes-4-139-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-4-139-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Wind Energ. Sci., 4, 139–161, 2019
https://doi.org/10.5194/wes-4-139-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-4-139-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article 14 Mar 2019
Research article | 14 Mar 2019
An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer
Mehdi Vali et al.Related authors
Sjoerd Boersma, Bart Doekemeijer, Mehdi Vali, Johan Meyers, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018, https://doi.org/10.5194/wes-3-75-2018, 2018
Short summary
Short summary
Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities such as the delivery of a demanded power is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, a control-oriented dynamical wind farm model is presented and validated with high-fidelity wind farm models. In contrast to the latter models, the model presented in this work is computationally efficient and hence suitable for online wind farm control.
Siamak Akbarzadeh, Hassan Kassem, Renko Buhr, Gerald Steinfeld, and Bernhard Stoevesandt
Wind Energ. Sci., 4, 619–632, https://doi.org/10.5194/wes-4-619-2019, https://doi.org/10.5194/wes-4-619-2019, 2019
Short summary
Short summary
The numerical flow simulation solvers are extensively used for site assessment in the wind energy industry. However, due to the complexity of flow regimes, it is essential to calibrate the important parameters of such algorithms with measurement data. In this paper, we present a computationally cheap (adjoint) solver that can be coupled with any standard gradient-based optimizer to calibrate the inflow boundary of a CFD solver using the wind speed measurements from the interior of a domain.
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.
Markus Sommerfeld, Martin Dörenkämper, Gerald Steinfeld, and Curran Crawford
Wind Energ. Sci., 4, 563–580, https://doi.org/10.5194/wes-4-563-2019, https://doi.org/10.5194/wes-4-563-2019, 2019
Short summary
Short summary
Airborne wind energy systems aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind measurements and computational simulations both have advantages and disadvantages when assessing the wind resource at such heights. This article investigates whether assimilating measurements into the model generates a more accurate wind data set up to 1100 m. These wind data sets are used to estimate optimal AWES operating altitudes and power.
Jörge Schneemann, Andreas Rott, Martin Dörenkämper, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-39, https://doi.org/10.5194/wes-2019-39, 2019
Revised manuscript accepted for WES
Short summary
Short summary
Offshore wind farm clusters cause reduced wind speeds in downstream regions which can extend over more than 50 km.
We analysed the impact of these so called cluster wakes on a distant wind farm using remote sensing wind measurements and power production data.
Cluster wakes caused power losses up to 55 km downstream in certain atmospheric states.
A better understanding of cluster wake effects reduces uncertainties in offshore wind resource assessment and improves offshore areal planning.
Róbert Ungurán, Vlaho Petrović, Lucy Y. Pao, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-15, https://doi.org/10.5194/wes-2019-15, 2019
Revised manuscript accepted for WES
Short summary
Short summary
A novel lidar based sensory system for wind turbine control is proposed. The main contributions are the parametrization method of the novel measurement system, identification of possible sources of measurement uncertainty as well as their modelling. Although not in the focus of the submitted paper, the mentioned contributions represent essential building blocks for robust feedback-feedforward wind turbine control development which could be used to improve wind turbine control strategies.
Andreas Rott, Bart Doekemeijer, Janna Kristina Seifert, Jan-Willem van Wingerden, and Martin Kühn
Wind Energ. Sci., 3, 869–882, https://doi.org/10.5194/wes-3-869-2018, https://doi.org/10.5194/wes-3-869-2018, 2018
Short summary
Short summary
Active wake deflection (AWD) aims to increase the power output of a wind farm by misaligning the yaw of upstream turbines. We analysed the effect of dynamic wind direction changes on AWD. The results show that AWD is very sensitive towards these dynamics. Therefore, we present a robust active wake control, which considers uncertainties and wind direction changes, increasing the overall power output of a wind farm. A side effect is a significant reduction of the yaw actuation of the turbines.
Bart M. Doekemeijer, Sjoerd Boersma, Lucy Y. Pao, Torben Knudsen, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 749–765, https://doi.org/10.5194/wes-3-749-2018, https://doi.org/10.5194/wes-3-749-2018, 2018
Short summary
Short summary
Most wind farm control algorithms in the literature rely on a simplified mathematical model that requires constant calibration to the current conditions. This paper provides such an estimation algorithm for a dynamic model capturing the turbine power production and flow field at hub height. Performance was demonstrated in high-fidelity simulations for two-turbine and nine-turbine farms, accurately estimating the ambient conditions and wind field inside the farms at a low computational cost.
Niko Mittelmeier and Martin Kühn
Wind Energ. Sci., 3, 395–408, https://doi.org/10.5194/wes-3-395-2018, https://doi.org/10.5194/wes-3-395-2018, 2018
Short summary
Short summary
Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield. This paper presents new methods to improve turbine alignment and detect changes during operational lifetime with standard nacelle met mast instruments. The flow distortion behind the rotor is corrected with a multilinear regression model and two alignment changes are detected with an accuracy of ±1.4° within 3 days of operation after the change is introduced.
Laura Valldecabres, Alfredo Peña, Michael Courtney, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 3, 313–327, https://doi.org/10.5194/wes-3-313-2018, https://doi.org/10.5194/wes-3-313-2018, 2018
Short summary
Short summary
This paper focuses on the use of scanning lidars for very short-term forecasting of wind speeds in a near-coastal area. An extensive data set of offshore lidar measurements up to 6 km has been used for this purpose. Using dual-doppler measurements, the topographic characteristics of the area have been modelled. Assuming Taylor's frozen turbulence and applying the topographic corrections, we demonstrate that we can forecast wind speeds with more accuracy than the benchmarks persistence or ARIMA.
Sjoerd Boersma, Bart Doekemeijer, Mehdi Vali, Johan Meyers, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018, https://doi.org/10.5194/wes-3-75-2018, 2018
Short summary
Short summary
Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities such as the delivery of a demanded power is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, a control-oriented dynamical wind farm model is presented and validated with high-fidelity wind farm models. In contrast to the latter models, the model presented in this work is computationally efficient and hence suitable for online wind farm control.
Lukas Vollmer, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 2, 603–614, https://doi.org/10.5194/wes-2-603-2017, https://doi.org/10.5194/wes-2-603-2017, 2017
Short summary
Short summary
A model chain to simulate changing atmospheric conditions at the location of an offshore wind farm is introduced and validated. The methodology is used to simulate the wind flow upstream and downstream of an offshore wind turbine of the German wind farm Alpha ventus. The model results show a good agreement with wind measurements from the met mast that is located at the wind farm and with remote sensing measurements of the horizontal wind field.
Davide Trabucchi, Lukas Vollmer, and Martin Kühn
Wind Energ. Sci., 2, 569–586, https://doi.org/10.5194/wes-2-569-2017, https://doi.org/10.5194/wes-2-569-2017, 2017
Short summary
Short summary
The wakes of wind turbines cause losses in the energy production of a wind farm. The accuracy of models applied to predict wake losses is a key factor for new wind projects. This paper presents an engineering wake model that can simulate merging wakes on the basis of physical principles. We used high-fidelity simulations of merging wakes to assess this model and found a better agreement with the reference than commonly used models implementing the superposition of individual wakes.
Niko Mittelmeier, Julian Allin, Tomas Blodau, Davide Trabucchi, Gerald Steinfeld, Andreas Rott, and Martin Kühn
Wind Energ. Sci., 2, 477–490, https://doi.org/10.5194/wes-2-477-2017, https://doi.org/10.5194/wes-2-477-2017, 2017
Short summary
Short summary
Stability classification is usually based on measurements from met masts, buoys or lidars. The objective of this paper is to find a classification for stability based on wind turbine supervisory control and data acquisition measurements in order to fit engineering wake models better to the current ambient conditions. The proposed signal is very sensitive to increased turbulence. It allows us to distinguish between conditions with different magnitudes of wake effects.
Marijn Floris van Dooren, Filippo Campagnolo, Mikael Sjöholm, Nikolas Angelou, Torben Mikkelsen, and Martin Kühn
Wind Energ. Sci., 2, 329–341, https://doi.org/10.5194/wes-2-329-2017, https://doi.org/10.5194/wes-2-329-2017, 2017
Short summary
Short summary
We conducted measurements in a wind tunnel with the remote sensing technique lidar to map the flow around a row of three model wind turbines. Two lidars were positioned near the wind tunnel walls to measure the two-dimensional wind vector over a defined scanning line or area without influencing the flow itself. A comparison of the lidar measurements with a hot-wire probe and a thorough uncertainty analysis confirmed the usefulness of lidar technology for such flow measurements in a wind tunnel.
Niko Mittelmeier, Tomas Blodau, and Martin Kühn
Wind Energ. Sci., 2, 175–187, https://doi.org/10.5194/wes-2-175-2017, https://doi.org/10.5194/wes-2-175-2017, 2017
Short summary
Short summary
Efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method presented estimates the environmental conditions from turbine states and uses pre-calculated power lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output are an indication of underperformance. A demonstration of the method's ability to detect underperformance is given.
Lukas Vollmer, Gerald Steinfeld, Detlev Heinemann, and Martin Kühn
Wind Energ. Sci., 1, 129–141, https://doi.org/10.5194/wes-1-129-2016, https://doi.org/10.5194/wes-1-129-2016, 2016
Short summary
Short summary
The wake flow downstream of yaw misaligned wind turbines is studied in numeric simulations of different atmospheric turbulence and shear conditions. We find that the average trajectory of the wake as well as the variation about this average is influenced by the thermal stability of the atmosphere. The results suggest that an intentional intervention in the yaw control of individual turbines to increase overall wind farm performance might be not successful during unstable thermal conditions.
Juan José Trujillo, Janna Kristina Seifert, Ines Würth, David Schlipf, and Martin Kühn
Wind Energ. Sci., 1, 41–53, https://doi.org/10.5194/wes-1-41-2016, https://doi.org/10.5194/wes-1-41-2016, 2016
Short summary
Short summary
We present the analysis of the trajectories followed by the wind, in the immediate vicinity, behind an offshore wind turbine and their dependence on its yaw misalignment. We apply wake tracking on wind fields measured with a lidar (light detection and ranging) system located at the nacelle of the wind turbine and pointing downstream. The analysis reveals discrepancies of the estimated mean wake paths against theoretical and wind tunnel experiments using different wake-tracking techniques.
Related subject area
Control and system identification
Validation of a lookup-table approach to modeling turbine fatigue loads in wind farms under active wake control
Wind direction estimation using SCADA data with consensus-based optimization
Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1
Uncertainties identification of the blade-mounted lidar-based inflow wind speed measurements for robust feedback-feedforward control synthesis
Robust active wake control in consideration of wind direction variability and uncertainty
Automatic detection and correction of pitch misalignment in wind turbine rotors
Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control
Control design, implementation, and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain
Wind tunnel study on power output and yaw moments for two yaw-controlled model wind turbines
Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines
Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data
System identification, fuzzy control and simulation of a kite power system with fixed tether length
A simulation study demonstrating the importance of large-scale trailing vortices in wake steering
Aero-elastic wind turbine design with active flaps for AEP maximization
Wind farms providing secondary frequency regulation: evaluating the performance of model-based receding horizon control
Field test of wake steering at an offshore wind farm
Iterative feedback tuning of wind turbine controllers
Articulated blade tip devices for load alleviation on wind turbines
Wind tunnel tests with combined pitch and free-floating flap control: data-driven iterative feedforward controller tuning
Periodic stability analysis of wind turbines operating in turbulent wind conditions
Basic controller tuning for large offshore wind turbines
Hector Mendez Reyes, Stoyan Kanev, Bart Doekemeijer, and Jan-Willem van Wingerden
Wind Energ. Sci., 4, 549–561, https://doi.org/10.5194/wes-4-549-2019, https://doi.org/10.5194/wes-4-549-2019, 2019
Short summary
Short summary
Within wind farms, the wind turbines interact with each other through their wakes. Turbines operating in these wakes have lower power production and increased wear and tear. Wake redirection is control strategy to steer the wakes aside from downstream turbines, increasing the power yield of the farm. Models for predicting the power gain and impacts on wear exist, but they are still immature and require validation. The validation of such a model is the purpose of this paper.
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.
Róbert Ungurán, Vlaho Petrović, Lucy Y. Pao, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-15, https://doi.org/10.5194/wes-2019-15, 2019
Revised manuscript accepted for WES
Short summary
Short summary
A novel lidar based sensory system for wind turbine control is proposed. The main contributions are the parametrization method of the novel measurement system, identification of possible sources of measurement uncertainty as well as their modelling. Although not in the focus of the submitted paper, the mentioned contributions represent essential building blocks for robust feedback-feedforward wind turbine control development which could be used to improve wind turbine control strategies.
Andreas Rott, Bart Doekemeijer, Janna Kristina Seifert, Jan-Willem van Wingerden, and Martin Kühn
Wind Energ. Sci., 3, 869–882, https://doi.org/10.5194/wes-3-869-2018, https://doi.org/10.5194/wes-3-869-2018, 2018
Short summary
Short summary
Active wake deflection (AWD) aims to increase the power output of a wind farm by misaligning the yaw of upstream turbines. We analysed the effect of dynamic wind direction changes on AWD. The results show that AWD is very sensitive towards these dynamics. Therefore, we present a robust active wake control, which considers uncertainties and wind direction changes, increasing the overall power output of a wind farm. A side effect is a significant reduction of the yaw actuation of the turbines.
Marta Bertelè, Carlo L. Bottasso, and Stefano Cacciola
Wind Energ. Sci., 3, 791–803, https://doi.org/10.5194/wes-3-791-2018, https://doi.org/10.5194/wes-3-791-2018, 2018
Short summary
Short summary
This work presents a new fully automated method to correct for
pitch misalignment imbalances of wind turbine rotors. The method
has minimal requirements, as it only assumes the availability of a
sensor of sufficient accuracy and bandwidth to detect the 1P
harmonic to the desired precision and the ability to command the
pitch setting of each blade independently from the others.
Extensive numerical simulations are used to demonstrate the new
procedure.
Bart M. Doekemeijer, Sjoerd Boersma, Lucy Y. Pao, Torben Knudsen, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 749–765, https://doi.org/10.5194/wes-3-749-2018, https://doi.org/10.5194/wes-3-749-2018, 2018
Short summary
Short summary
Most wind farm control algorithms in the literature rely on a simplified mathematical model that requires constant calibration to the current conditions. This paper provides such an estimation algorithm for a dynamic model capturing the turbine power production and flow field at hub height. Performance was demonstrated in high-fidelity simulations for two-turbine and nine-turbine farms, accurately estimating the ambient conditions and wind field inside the farms at a low computational cost.
Sebastiaan Paul Mulders, Niels Frederik Boudewijn Diepeveen, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 615–638, https://doi.org/10.5194/wes-3-615-2018, https://doi.org/10.5194/wes-3-615-2018, 2018
Short summary
Short summary
The modeling, operating strategy, and controller design for an actual in-field wind turbine with a fixed-displacement hydraulic drivetrain are presented. An analysis is given on a passive torque control strategy for below-rated operation. The turbine lacks the option to influence the system torque by a generator, so the turbine is regulated by a spear valve in the region between below- and above-rated operation. The control design is evaluated on a real-world 500 kW hydraulic wind turbine.
Jan Bartl, Franz Mühle, and Lars Sætran
Wind Energ. Sci., 3, 489–502, https://doi.org/10.5194/wes-3-489-2018, https://doi.org/10.5194/wes-3-489-2018, 2018
Short summary
Short summary
Our experimental wind tunnel study on a pair of model wind turbines demonstrates a significant potential of turbine yaw angle control for the combined optimization of turbine power and rotor loads. Depending on the turbines' relative positions to the incoming wind, a combined power increase and individual rotor load reduction can be achieved by operating the turbine rotors slightly misaligned with the main wind direction (i.e., at a certain yaw angle).
Wim Munters and Johan Meyers
Wind Energ. Sci., 3, 409–425, https://doi.org/10.5194/wes-3-409-2018, https://doi.org/10.5194/wes-3-409-2018, 2018
Short summary
Short summary
Wake interactions in wind farms result in power losses for downstream turbines. We aim to mitigate these losses through coordinated control of the induced slowdown of the wind by each turbine. We further analyze results from earlier work towards the utilization of such control strategies in practice. Coherent vortex shedding is identified and mimicked by a sinusoidal control. The latter is shown to increase power in downstream turbines and is robust to turbine spacing and turbulence intensity.
Niko Mittelmeier and Martin Kühn
Wind Energ. Sci., 3, 395–408, https://doi.org/10.5194/wes-3-395-2018, https://doi.org/10.5194/wes-3-395-2018, 2018
Short summary
Short summary
Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield. This paper presents new methods to improve turbine alignment and detect changes during operational lifetime with standard nacelle met mast instruments. The flow distortion behind the rotor is corrected with a multilinear regression model and two alignment changes are detected with an accuracy of ±1.4° within 3 days of operation after the change is introduced.
Tarek N. Dief, Uwe Fechner, Roland Schmehl, Shigeo Yoshida, Amr M. M. Ismaiel, and Amr M. Halawa
Wind Energ. Sci., 3, 275–291, https://doi.org/10.5194/wes-3-275-2018, https://doi.org/10.5194/wes-3-275-2018, 2018
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.
Michael K. McWilliam, Thanasis K. Barlas, Helge A. Madsen, and Frederik Zahle
Wind Energ. Sci., 3, 231–241, https://doi.org/10.5194/wes-3-231-2018, https://doi.org/10.5194/wes-3-231-2018, 2018
Short summary
Short summary
Maximizing wind energy production is challenging because the winds are always changing. Design optimization was used to explore how flaps can give rotor design engineers greater ability to adapt the rotor for different conditions. For rotors designed for peak efficiency (i.e. older designs) the flap adds 0.5 % improvement in energy production. However, for modern designs that optimize both the performance and the structure, the flap can provide a 1 % improvement.
Carl R. Shapiro, Johan Meyers, Charles Meneveau, and Dennice F. Gayme
Wind Energ. Sci., 3, 11–24, https://doi.org/10.5194/wes-3-11-2018, https://doi.org/10.5194/wes-3-11-2018, 2018
Short summary
Short summary
We investigate the capability of wind farms to track a power reference signal to help ensure reliable power grid operations. The wind farm controller is based on a simple dynamic wind farm model and tested using high-fidelity simulations. We find that the dynamic nature of the wind farm model is vital for tracking the power signal, and the controlled wind farm would pass industry performance tests in most cases.
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.
Edwin van Solingen, Sebastiaan Paul Mulders, and Jan-Willem van Wingerden
Wind Energ. Sci., 2, 153–173, https://doi.org/10.5194/wes-2-153-2017, https://doi.org/10.5194/wes-2-153-2017, 2017
Short summary
Short summary
The aim of this paper is to show that with an automated tuning strategy, wind turbine control performance can be significantly increased. To this end, iterative feedback tuning (IFT) is applied to two different turbine controllers. The results obtained by high-fidelity simulations indicate significant performance improvements over baseline controllers. It is concluded that IFT of turbine controllers has the potential to become a valuable tool for improving wind turbine performance.
Carlo L. Bottasso, Alessandro Croce, Federico Gualdoni, Pierluigi Montinari, and Carlo E. D. Riboldi
Wind Energ. Sci., 1, 297–310, https://doi.org/10.5194/wes-1-297-2016, https://doi.org/10.5194/wes-1-297-2016, 2016
Short summary
Short summary
The paper discusses different concepts for reducing loads on wind turbines using movable blade tips. Passive and semi-passive tip solutions move freely in response to air load fluctuations, while in the active case an actuator drives the tip motion in response to load measurements. The various solutions are compared with a standard blade and with each other in terms of their ability to reduce both fatigue and extreme loads.
Sachin T. Navalkar, Lars O. Bernhammer, Jurij Sodja, Edwin van Solingen, Gijs A. M. van Kuik, and Jan-Willem van Wingerden
Wind Energ. Sci., 1, 205–220, https://doi.org/10.5194/wes-1-205-2016, https://doi.org/10.5194/wes-1-205-2016, 2016
Short summary
Short summary
In order to reduce the cost of wind energy, it is necessary to reduce the loads that wind turbines withstand over their lifetime. The combination of blade rotation with newly designed blade shape changing actuators is demonstrated experimentally. While load reduction is achieved, the additional flexibility implies that careful control design is needed to avoid instability.
Riccardo Riva, Stefano Cacciola, and Carlo Luigi Bottasso
Wind Energ. Sci., 1, 177–203, https://doi.org/10.5194/wes-1-177-2016, https://doi.org/10.5194/wes-1-177-2016, 2016
Short summary
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This paper presents a method to assess the stability of a wind turbine. The proposed approach uses the recorded time history of the system response and fits to it a periodic reduced-order model that can handle stochastic disturbances. Stability is computed by using Floquet theory on the reduced-order model. Since the method only uses response data, it is applicable to any simulation model as well as to experimental test data. The method is compared to the well-known operational modal analysis.
Karl O. Merz
Wind Energ. Sci., 1, 153–175, https://doi.org/10.5194/wes-1-153-2016, https://doi.org/10.5194/wes-1-153-2016, 2016
Short summary
Short summary
Wind turbines are controlled through the electrical torque on the generator and the pitch of the blades. The tuning of the controller determines the dynamics of the system, which can then be good (smooth yet responsive) or bad (ineffective or unstable). A methodical investigation was conducted to determine the minimal model of the wind turbine structure and aerodynamics that can be used to tune the controller gains for large, multi-MW offshore wind turbines.
Cited articles
Aho, J., Buckspan, A., Laks, J., Fleming, P. A., Jeong, Y., Dunne, F.,
Churchfield, M., Pao, L. Y., and Johnson, K.: A tutorial of wind turbine
control for supporting grid frequency through active power control, in:
American Control Conference, IEEE, Montreal, QC, Canada, 3120–3131,
https://doi.org/10.1109/ACC.2012.6315180, 2012. a
Aho, J., Pao, L. Y., and Fleming, P. A.: An active power control system for
wind turbines capable of primary and secondary frequency control for
supporting grid reliability, in: 51st AIAA Aerospace Sciences Meeting
including the New Horizons Forum and Aerospace Exposition, Grapevine, Texas, 1–13,
https://doi.org/10.2514/6.2013-456, 2013. a
Aho, J., Fleming, P. A., and Pao, L. Y.: Active power control of wind
turbines
for ancillary services: A comparison of pitch and torque control
methodologies, in: American Control Conference, IEEE, Boston, MA, USA, 1407–1412,
https://doi.org/10.1109/ACC.2016.7525114, 2016. a, b
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, 2016. 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
Bay, C. J., Annoni, J., Taylor, T., Pao, L., and Johnson, K.: Active power
control for wind farms using distributed model predictive control and nearest
neighbor communication, in: American Control Conference, IEEE, Milwaukee, WI, USA, 682–687,
https://doi.org/10.23919/ACC.2018.8431764, 2018. a, b
Boersma, S., Vali, M., Kühn, M., and van Wingerden, J.-W.: Quasi Linear
Parameter Varying modeling for wind farm control using the 2D
Navier-Stokes equations, in: American Control Conference, IEEE, Boston, MA, USA, 4409–4414,
https://doi.org/10.1109/ACC.2016.7525616, 2016. a
Boersma, S., Doekemeijer, B., Vali, M., Meyers, J., and van Wingerden, J.-W.:
A control-oriented dynamic wind farm model: WFSim, Wind Energ. Sci., 3,
75–95, https://doi.org/10.5194/wes-3-75-2018, 2018. a
Boersma, S., Doekemeijer, B., Siniscalchi-Minna, S., and van Wingerden,
J.-W.:
A constrained wind farm controller providing secondary frequency regulation:
An LES study, Renew. Energ., 134, 639–652,
https://doi.org/10.1016/j.renene.2018.11.031, 2019. a
Campagnolo, F., Petrović, V., Schreiber, J., Nanos, E. M., Croce, A., and
Bottasso, C. L.: Wind tunnel testing of a closed-loop wake deflection
controller for wind farm power maximization, J. Phys. Conf.
Ser., 753, 032006, https://doi.org/10.1088/1742-6596/753/3/032006, 2016. a
Ciri, U., Rotea, M., Santoni, C., and Leonardi, S.: Large-eddy simulations
with
extremum-seeking control for individual wind turbine power optimization, Wind
Energy, 20, 1617–1634, https://doi.org/10.1002/we.2112, 2017. a, b
de Alegria, I. M., Andreu, J., Martin, J. L., Ibanez, P., Villate, J. L., and
Camblong, H.: Connection requirements for wind farms: A survey on technical
requierements and regulation, Renewable and Sustainable Energy Reviews, 11,
1858–1872, https://doi.org/10.1016/j.rser.2006.01.008, 2007. a
Doekemeijer, B. M., Boersma, S., Pao, L. Y., Knudsen, T., and van Wingerden,
J.-W.: Online model calibration for a simplified LES model in pursuit of
real-time closed-loop wind farm control, Wind Energ. Sci., 3, 749–765,
https://doi.org/10.5194/wes-3-749-2018, 2018. a
Fleming, P. A., 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
Frandsen, S.: Turbulence and turbulence-generated structural loading in wind
turbine clusters, PhD thesis, Risø-R-1188(EN), Technical University of
Denmark, 2007. a
Gebraad, P. M. O. and van Wingerden, J.-W.: Maximum power-point tracking
control for wind farms, Wind Energy, 18, 429–447, https://doi.org/10.1002/we.1706,
2015. 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, we.1822,
2016. a
Goit, J. P. and Meyers, J.: Optimal control of energy extraction in wind-farm
boundary layers, J. Fluid Mech., 768, 5–50,
https://doi.org/10.1017/jfm.2015.70, 2015. a, b
IEC: International Electrotechnical Commission, Wind turbines – Part 1:
Design requirements, IEC 61400-1:2005(E), Geneva, Switzerland, 3rd edn.,
2005. a
Jensen, T. N., Knudsen, T., and Bak, T.: Fatigue minimising power reference
control of a de-rated wind farm, J. Phys. Conf. Ser., 753,
052022, https://doi.org/10.1088/1742-6596/753/5/052022, 2016. a
Johnson, K. E. and Fritsch, G.: Assessment of extremum seeking control for
wind
farm energy production, Wind Engineering, 36, 701–716,
https://doi.org/10.1260/0309-524X.36.6.701, 2012. a
Kanev, S. K., Savenije, F. J., and Engels, W. P.: Active wake control: An
approach to optimize the lifetime operation of wind farms, Wind Energy, 21,
488–501, https://doi.org/10.1002/we.2173, 2018. a
Knudsen, T., Bak, T., and Svenstrup, M.: Survey of wind farm control: power
and
fatigue optimization, Wind Energy, 18, 1333–1351, https://doi.org/10.1002/we.1760,
2015. a
Madjidian, D.: Scalable minimum fatigue control of dispatchable wind farms,
Wind Energy, 19, 1933–1944, https://doi.org/10.1002/we.1960, 2016. a
Marden, J. R., Ruben, S. D., and Pao, L. Y.: A model-free approach to wind
farm
control using game theoretic methods, IEEE T. Cont. Syst.
T., 21, 1207–1214, https://doi.org/10.1109/TCST.2013.2257780, 2013. a
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F.,
Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The
Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric
and oceanic flows: model formulation, recent developments, and future
perspectives, Geosci. Model Dev., 8, 2515–2551,
https://doi.org/10.5194/gmd-8-2515-2015, 2015. a, b
Meyers, J. and Meneveau, C.: Large Eddy Simulations of large wind-turbine
arrays in the atmospheric boundary layer, 48th AIAA Aerospace Sciences
Meeting Including the New Horizons Forum and Aerospace Exposition, Aerospace
Sciences Meetings, Orlando, Florida, https://doi.org/10.2514/6.2010-827, 2010. a
Munters, W. and Meyers, J.: Dynamic strategies for yaw and induction control
of
wind farms based on large-eddy simulation and optimization, Energies, 11,
177, https://doi.org/10.3390/en11010177, 2018. a, b
Petrović, V., Schottler, J., Neunaber, I., Hölling, M., and Kühn,
M.: Wind tunnel validation of a closed loop active power control for wind
farms, J. Phys. Conf. Ser., 1037, 032020,
https://doi.org/10.1088/1742-6596/1037/3/032020, 2018. a
Pilong, C.: PJM Manual 12: Balancing Operations, 30th edn., PJM, Audubon, PA,
USA, 2013. a
Porté-Agel, F., Wu, Y.-T., Lu, H., and Conzemius, R. J.: Large-eddy
simulation of atmospheric boundary layer flow through wind turbines and wind
farms, J. Wind Eng. Ind. Aerod., 99, 154–168,
https://doi.org/10.1016/j.jweia.2011.01.011, 2011. a
Rott, A., Boersma, S., van Wingerden, J.-W., and Kühn, M.: Dynamic flow
model for real-time application in wind farm control, J. Phys.
Conf. Ser., 854, 012039, https://doi.org/10.1088/1742-6596/854/1/012039, 2017. a
Sanderse, B.: Aerodynamics of wind turbine wakes, Energy Research Center of
the Netherlands (ECN), ECN-E-09-016, Petten, the Netherlands, Tech. Rep.
2009. a
Sarlak, H.: Large eddy simulation of turbulent flows in wind energy, PhD
thesis, DTU Wind Energy PhD-0037(EN), Technical University of Denmark,
2014. a
Schepers, J. G. and van der Pijl, S. P.: Improved modelling of wake
aerodynamics and assessment of new farm control strategies, J.
Phys. Conf. Ser., 75, 012039,
https://doi.org/10.1088/1742-6596/75/1/012039, 2007. a
Schumann, U.: Subgrid scale model for finite difference simulations of
turbulent flows in plane channels and annuli, J. Comput.
Phys., 18, 376–404, https://doi.org/10.1016/0021-9991(75)90093-5, 1975. a
Shapiro, C. R., Bauweraerts, P., Meyers, J., Meneveau, C., and Gayme, D. F.:
Model-based receding horizon control of wind farms for secondary frequency
regulation, Wind Energy, 20, 1261–1275, https://doi.org/10.1002/we.2093, 2017. a, b, c, d
Soleimanzadeh, M., Wisniewski, R., and Johnson, K.: A distributed
optimization
framework for wind farms, J. Wind Eng. Ind.
Aerod., 123, 88–98, https://doi.org/10.1016/j.jweia.2013.08.011, 2013. a
Spudić, V., Conte, C., Baotić, M., and Morari, M.: Cooperative
distributed model predictive control for wind farms, Optim. Contr.
Appl. Met., 36, 333–352, https://doi.org/10.1002/oca.2136, 2015. a
Vali, M., van Wingerden, J.-W., and Kühn, M.: Optimal multivariable
individual pitch control for load reduction of large wind turbines, in:
American Control Conference, IEEE, Boston, MA, USA, 3163–3169,
https://doi.org/10.1109/ACC.2016.7525404, 2016. a
Vali, M., Vollmer, L., Petrović, V., and Kühn, M.: A closed-loop wind
farm control framework for maximization of wind farm power production, in:
Wind Energy Science Conference, EAWE, Lyngby, Denmark, 2017. a
Vali, M., Petrović, V., Boersma, S., van Wingerden, J.-W., Pao, L. Y.,
and
Kühn, M.: Model predictive active power control of waked wind farms, in:
American Control Conference, IEEE, Milwaukee, WI, USA, 707–714, https://doi.org/10.23919/ACC.2018.8431391,
2018a. a, b, c, d
Vali, M., Petrović, V., Boersma, S., van Wingerden, J.-W., Pao, L. Y.,
and
Kühn, M.: Adjoint-based model predictive control for optimal energy
extraction in waked wind farms, Control Eng. Pract., 84, 48–62,
https://doi.org/10.1016/j.conengprac.2018.11.005, 2019. a, b, c, d
van Dijk, M. T., van Wingerden, J.-W., Ashuri, T., and Li, Y.: Wind farm
multi-objective wake redirection for optimizing power production and loads,
Energy, 121, 561–569, https://doi.org/10.1016/j.energy.2017.01.051, 2017. a
Wagenaar, J., Machielse, L., and Schepers, J.: Controlling wind in ECN's
scaled wind farm, Proc. Europe's Premier Wind Energy Event, EWEA, Copenhagen,
Denmark, 685–694, 2012. a
Witha, B., Steinfeld, G., Dörenkämper, M., and Heinemann, D.:
Large-eddy simulation of multiple wakes in offshore wind farms, J.
Phys. Conf. Ser., 555, 012108,
https://doi.org/10.1088/1742-6596/555/1/012108, 2014. a, b
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Short summary
A new active power control (APC) approach is investigated to simultaneously reduce the wake-induced power tracking errors and structural fatigue loads of individual turbines within a wind farm. The non-unique solution of the APC problem with respect to the distribution of the individual powers is exploited. The simple control architecture and practical measurement system make the proposed approach prominent for real-time control of large wind farms with turbulent flows and wakes.
A new active power control (APC) approach is investigated to simultaneously reduce the...
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