Articles | Volume 7, issue 4
https://doi.org/10.5194/wes-7-1753-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1753-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Aug 2022
Research article |
| 24 Aug 2022
| 24 Aug 2022
Demonstration of a fault impact reduction control module for wind turbines
Benjamin Anderson
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO, USA
Edward Baring-Gould
National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO, USA
Related authors
Kelsey Shaler, Benjamin Anderson, Luis A. Martínez-Tossas, Emmanuel Branlard, and Nick Johnson
Wind Energ. Sci., 8, 383–399, https://doi.org/10.5194/wes-8-383-2023, https://doi.org/10.5194/wes-8-383-2023, 2023
Short summary
Short summary
Free-vortex wake (OLAF) and low-fidelity blade-element momentum (BEM) structural results are compared to high-fidelity simulation results for a flexible downwind turbine for varying inflow conditions. Overall, OLAF results were more consistent than BEM results when compared to SOWFA results under challenging inflow conditions. Differences between OLAF and BEM results were dominated by yaw misalignment angle, with varying shear exponent and turbulence intensity causing more subtle differences.
Ram C. Poudel, David Corbus, and Ian Baring-Gould
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-45, https://doi.org/10.5194/wes-2023-45, 2023
Revised manuscript not accepted
Short summary
Short summary
We propose a new method to synthesize 1 Hz wind speed and wind power time series data from the industry standard 10-minute wind turbine performance data. The method is based on a parameterized power spectral density (PSD) function decomposed into trend and random components. We illustrate the intra-timestep data synthesis utilizing 1 Hz data from two distributed wind turbines.
Kelsey Shaler, Benjamin Anderson, Luis A. Martínez-Tossas, Emmanuel Branlard, and Nick Johnson
Wind Energ. Sci., 8, 383–399, https://doi.org/10.5194/wes-8-383-2023, https://doi.org/10.5194/wes-8-383-2023, 2023
Short summary
Short summary
Free-vortex wake (OLAF) and low-fidelity blade-element momentum (BEM) structural results are compared to high-fidelity simulation results for a flexible downwind turbine for varying inflow conditions. Overall, OLAF results were more consistent than BEM results when compared to SOWFA results under challenging inflow conditions. Differences between OLAF and BEM results were dominated by yaw misalignment angle, with varying shear exponent and turbulence intensity causing more subtle differences.
Alessandro Bianchini, Galih Bangga, Ian Baring-Gould, Alessandro Croce, José Ignacio Cruz, Rick Damiani, Gareth Erfort, Carlos Simao Ferreira, David Infield, Christian Navid Nayeri, George Pechlivanoglou, Mark Runacres, Gerard Schepers, Brent Summerville, David Wood, and Alice Orrell
Wind Energ. Sci., 7, 2003–2037, https://doi.org/10.5194/wes-7-2003-2022, https://doi.org/10.5194/wes-7-2003-2022, 2022
Short summary
Short summary
The paper is part of the Grand Challenges Papers for Wind Energy. It provides a status of small wind turbine technology in terms of technical maturity, diffusion, and cost. Then, five grand challenges that are thought to be key to fostering the development of the technology are proposed. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Improvement areas are highlighted, within which 10 key enabling actions are finally proposed to the wind community.
Emmanuel Branlard, Ian Brownstein, Benjamin Strom, Jason Jonkman, Scott Dana, and Edward Ian Baring-Gould
Wind Energ. Sci., 7, 455–467, https://doi.org/10.5194/wes-7-455-2022, https://doi.org/10.5194/wes-7-455-2022, 2022
Short summary
Short summary
In this work, we present an aerodynamic tool that can model an arbitrary collections of wings, blades, rotors, and towers. With these functionalities, the tool can be used to study and design advanced wind energy concepts, such as horizontal-axis wind turbines, vertical-axis wind turbines, kites, or multi-rotors. This article describes the key features of the tool and presents multiple applications. Field measurements of horizontal- and vertical-axis wind turbines are used for comparison.
Related subject area
Thematic area: Dynamics and control | Topic: Wind turbine control
The potential of wave feedforward control for floating wind turbines: a wave tank experiment
Assessing the impact of waves and platform dynamics on floating wind-turbine energy production
Combining wake redirection and derating strategies in a load-constrained wind farm power maximization
On the robustness of a blade load-based wind speed estimator to dynamic pitch control strategies
Multi-objective calibration of vertical-axis wind turbine controllers: balancing aero-servo-elastic performance and noise
Feedforward pitch control for a 15 MW wind turbine using a spinner-mounted single-beam lidar
Wind vane correction during yaw misalignment for horizontal-axis wind turbines
Increased power gains from wake steering control using preview wind direction information
Analysis and multi-objective optimisation of wind turbine torque control strategies
Damping analysis of floating offshore wind turbines (FOWTs): a new control strategy reducing the platform vibrations
Assessing lidar-assisted feedforward and multivariable feedback controls for large floating wind turbines
Prognostics-based adaptive control strategy for lifetime control of wind turbines
Platform yaw drift in upwind floating wind turbines with single-point-mooring system and its mitigation by individual pitch control
Evaluation of lidar-assisted wind turbine control under various turbulence characteristics
FarmConners wind farm flow control benchmark – Part 1: Blind test results
Lidar-assisted model predictive control of wind turbine fatigue via online rainflow counting considering stress history
Amr Hegazy, Peter Naaijen, Vincent Leroy, Félicien Bonnefoy, Mohammad Rasool Mojallizadeh, Yves Pérignon, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 1669–1688, https://doi.org/10.5194/wes-9-1669-2024, https://doi.org/10.5194/wes-9-1669-2024, 2024
Short summary
Short summary
Successful wave tank experiments were conducted to evaluate the feedforward (FF) control strategy benefits in terms of structural loads and power quality of floating wind turbine components. The wave FF control strategy is effective when it comes to alleviating the effects of the wave forces on the floating offshore wind turbines, whereas wave FF control requires a significant amount of actuation to minimize the platform pitch motion, which makes such technology unfavorable for that objective.
Alessandro Fontanella, Giorgio Colpani, Marco De Pascali, Sara Muggiasca, and Marco Belloli
Wind Energ. Sci., 9, 1393–1417, https://doi.org/10.5194/wes-9-1393-2024, https://doi.org/10.5194/wes-9-1393-2024, 2024
Short summary
Short summary
Waves can boost a floating wind turbine's power output by moving its rotor against the wind. Studying this, we used four models to explore the impact of waves and platform dynamics on turbines in the Mediterranean. We found that wind turbulence, not waves, primarily affects power fluctuations. In real conditions, floating wind turbines produce less energy compared to fixed-bottom ones, mainly due to platform tilt.
Alessandro Croce, Stefano Cacciola, and Federico Isella
Wind Energ. Sci., 9, 1211–1227, https://doi.org/10.5194/wes-9-1211-2024, https://doi.org/10.5194/wes-9-1211-2024, 2024
Short summary
Short summary
For a few years now, various techniques have been studied to maximize the energy production of a wind farm, that is, from a system consisting of several wind turbines. These wind farm controller techniques are often analyzed individually and can generate loads higher than the design ones on the individual wind turbine. In this paper we study the simultaneous use of two different techniques with the goal of finding the optimal combination that at the same time preserves the design loads.
Marion Coquelet, Maxime Lejeune, Laurent Bricteux, Aemilius A. W. van Vondelen, Jan-Willem van Wingerden, and Philippe Chatelain
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-56, https://doi.org/10.5194/wes-2024-56, 2024
Revised manuscript accepted for WES
Short summary
Short summary
An extended Kalman filter is used to estimate the wind impinging on a wind turbine based on the blade bending moments and a turbine model. Using Large-Eddy Simulations, this paper verifies how robust the estimator is to the turbine control strategy, as it impacts loads and operating parameters. It is shown that including dynamics in the turbine model to account for delays between actuation and bending moments is needed to maintain the accuracy of the estimator when dynamic pitch control is used.
Livia Brandetti, Sebastiaan Paul Mulders, Roberto Merino-Martinez, Simon Watson, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 471–493, https://doi.org/10.5194/wes-9-471-2024, https://doi.org/10.5194/wes-9-471-2024, 2024
Short summary
Short summary
This research presents a multi-objective optimisation approach to balance vertical-axis wind turbine (VAWT) performance and noise, comparing the combined wind speed estimator and tip-speed ratio (WSE–TSR) tracking controller with a baseline. Psychoacoustic annoyance is used as a novel metric for human perception of wind turbine noise. Results showcase the WSE–TSR tracking controller’s potential in trading off the considered objectives, thereby fostering the deployment of VAWTs in urban areas.
Wei Fu, Feng Guo, David Schlipf, and Alfredo Peña
Wind Energ. Sci., 8, 1893–1907, https://doi.org/10.5194/wes-8-1893-2023, https://doi.org/10.5194/wes-8-1893-2023, 2023
Short summary
Short summary
A high-quality preview of the rotor-effective wind speed is a key element of the benefits of feedforward pitch control. We model a one-beam lidar in the spinner of a 15 MW wind turbine. The lidar rotates with the wind turbine and scans the inflow in a circular pattern, mimicking a multiple-beam lidar at a lower cost. We found that a spinner-based one-beam lidar provides many more control benefits than the one on the nacelle, which is similar to a four-beam nacelle lidar for feedforward control.
Andreas Rott, Leo Höning, Paul Hulsman, Laura J. Lukassen, Christof Moldenhauer, and Martin Kühn
Wind Energ. Sci., 8, 1755–1770, https://doi.org/10.5194/wes-8-1755-2023, https://doi.org/10.5194/wes-8-1755-2023, 2023
Short summary
Short summary
This study examines wind vane measurements of commercial wind turbines and their impact on yaw control. The authors discovered that rotor interference can cause an overestimation of wind vane measurements, leading to overcorrection of the yaw controller. A correction function that improves the yaw behaviour is presented and validated in free-field experiments on a commercial wind turbine. This work provides new insights into wind direction measurements and suggests ways to optimize yaw control.
Balthazar Arnoldus Maria Sengers, Andreas Rott, Eric Simley, Michael Sinner, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 8, 1693–1710, https://doi.org/10.5194/wes-8-1693-2023, https://doi.org/10.5194/wes-8-1693-2023, 2023
Short summary
Short summary
Unexpected wind direction changes are undesirable, especially when performing wake steering. This study explores whether the yaw controller can benefit from accessing wind direction information before a change reaches the turbine. Results from two models with different fidelities demonstrate that wake steering can indeed benefit from preview information.
Livia Brandetti, Sebastiaan Paul Mulders, Yichao Liu, Simon Watson, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1553–1573, https://doi.org/10.5194/wes-8-1553-2023, https://doi.org/10.5194/wes-8-1553-2023, 2023
Short summary
Short summary
This research presents the additional benefits of applying an advanced combined wind speed estimator and tip-speed ratio tracking (WSE–TSR) controller compared to the baseline Kω2. Using a frequency-domain framework and an optimal calibration procedure, the WSE–TSR tracking control scheme shows a more flexible trade-off between conflicting objectives: power maximisation and load minimisation. Therefore, implementing this controller on large-scale wind turbines will facilitate their operation.
Matteo Capaldo and Paul Mella
Wind Energ. Sci., 8, 1319–1339, https://doi.org/10.5194/wes-8-1319-2023, https://doi.org/10.5194/wes-8-1319-2023, 2023
Short summary
Short summary
The controller impacts the movements, loads and yield of wind turbines.
Standard controllers are not adapted for floating, and they can lead to underperformances and overloads. New control strategies, considering the coupling between the floating dynamics and the rotor dynamics, are necessary to reduce platform movements and to improve performances. This work proposes a new control strategy adapted to floating wind, showing a reduction in loads without affecting the power production.
Feng Guo and David Schlipf
Wind Energ. Sci., 8, 1299–1317, https://doi.org/10.5194/wes-8-1299-2023, https://doi.org/10.5194/wes-8-1299-2023, 2023
Short summary
Short summary
This paper assesses lidar-assisted collective pitch feedforward (LACPF) and multi-variable feedback (MVFB) controls for the IEA 15.0 MW reference turbine. The main contributions of this work include (a) optimizing a four-beam pulsed lidar for a large turbine, (b) optimal tuning of speed regulation gains and platform feedback gains for the MVFB and LACPF controllers, and (c) assessing the benefits of the two control strategies using realistic offshore turbulence spectral characteristics.
Edwin Kipchirchir, M. Hung Do, Jackson G. Njiri, and Dirk Söffker
Wind Energ. Sci., 8, 575–588, https://doi.org/10.5194/wes-8-575-2023, https://doi.org/10.5194/wes-8-575-2023, 2023
Short summary
Short summary
In this work, an adaptive control strategy for controlling the lifetime of wind turbine components is proposed. Performance of the lifetime controller is adapted based on real-time health status of the rotor blades to guarantee a predefined lifetime. It shows promising results in lifetime control of blades without speed regulation and tower load mitigation trade-off. It can be applied in optimizing maintenance scheduling of wind farms, which increases reliability and reduces maintenance costs.
Iñaki Sandua-Fernández, Felipe Vittori, Raquel Martín-San-Román, Irene Eguinoa, and José Azcona-Armendáriz
Wind Energ. Sci., 8, 277–288, https://doi.org/10.5194/wes-8-277-2023, https://doi.org/10.5194/wes-8-277-2023, 2023
Short summary
Short summary
This work analyses in detail the causes of the yaw drift in floating offshore wind turbines with a single-point-mooring system induced by an upwind wind turbine. The ability of an individual pitch control strategy based on yaw misalignment is demonstrated through simulations using the NREL 5 MW wind turbine mounted on a single-point-mooring version of the DeepCwind OC4 floating platform. This effect is considered to be relevant for all single-point-moored concepts.
Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 8, 149–171, https://doi.org/10.5194/wes-8-149-2023, https://doi.org/10.5194/wes-8-149-2023, 2023
Short summary
Short summary
The benefits of lidar-assisted control are evaluated using both the Mann model and Kaimal model-based 4D turbulence, considering the variation of turbulence parameters. Simulations are performed for the above-rated mean wind speed, using the NREL 5.0 MW reference wind turbine and a four-beam lidar system. Using lidar-assisted control reduces the variations in rotor speed, pitch rate, tower base fore–aft bending moment, and electrical power significantly.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
Short summary
Short summary
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Stefan Loew and Carlo L. Bottasso
Wind Energ. Sci., 7, 1605–1625, https://doi.org/10.5194/wes-7-1605-2022, https://doi.org/10.5194/wes-7-1605-2022, 2022
Short summary
Short summary
This publication presents methods to improve the awareness and control of material fatigue for wind turbines. This is achieved by enhancing a sophisticated control algorithm which utilizes wind prediction information from a laser measurement device. The simulation results indicate that the novel algorithm significantly improves the economic performance of a wind turbine. This benefit is particularly high for situations when the prediction quality is low or the prediction time frame is short.
Cited articles
Anderson, B.: Simulink model of CART wind turbine with Fault Impact
Reduction Control Module, Zenodo [data set], https://doi.org/10.5281/zenodo.5604464, 2021a. a
Anderson, B.: Simulink model of CART wind turbine with Fault Impact Reduction Control Module (2.0.4), Zenodo [code, data set], https://doi.org/10.5281/zenodo.6954058, 2021b. a
Fingersh, L. J. and Johnson, K.: Controls Advanced Research Turbine
(CART) Commissioning and Baseline Data Collection, Tech. Rep.,
NREL/TP-500-32879, 15002211, https://doi.org/10.2172/15002211, 2002. a
García Márquez, F. P., Tobias, A. M., Pinar Pérez, J. M., and Papaelias, M.:
Condition monitoring of wind turbines: Techniques and methods, Renew. Energ., 46, 169–178, https://doi.org/10.1016/j.renene.2012.03.003, 2012. a
Hameed, Z., Hong, Y., Cho, Y., Ahn, S., and Song, C.: Condition monitoring and
fault detection of wind turbines and related algorithms: A review,
Renewable and Sustainable Energy Reviews, 13, 1–39,
https://doi.org/10.1016/j.rser.2007.05.008, 2009. a
Liu, Y., Frederik, J., Ferrari, R. M., Wu, P., Li, S., and van Wingerden,
J.-W.: Fault-tolerant individual pitch control of floating offshore wind
turbines via subspace predictive repetitive control, Wind Energy, 24,
1045–1065, https://doi.org/10.1002/we.2616, eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/we.2616 (last access: 26 October 2021), 2021. a, b
NOA: IEEE Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces, IEEE Std 1547–2018 (Revision of IEEE Std 1547–2003), 6 April 2018, 1–138, https://doi.org/10.1109/IEEESTD.2018.8332112, 2018. a
Odgaard, P. and Johnson, K.: Wind turbine fault detection and fault tolerant
control – An enhanced benchmark challenge, 2013 American Control
Conference, 17–19 June 2013, 4447–4452, https://doi.org/10.1109/ACC.2013.6580525, 2013. a
Odgaard, P. and Stoustrup, J.: Fault tolerant wind farm control – A benchmark model, 2013 IEEE International Conference on Control Applications (CCA), 28–30 August 2013, 412–417, https://doi.org/10.1109/CCA.2013.6662784, 2013. a
Odgaard, P. F., Stoustrup, J., and Kinnaert, M.: Fault Tolerant Control of
Wind Turbines – a benchmark model, IFAC Proceedings Volumes, 42,
155–160, https://doi.org/10.3182/20090630-4-ES-2003.00026, 2009. a
Odgaard, P. F., Stoustrup, J., and Kinnaert, M.: Fault-Tolerant Control of
Wind Turbines: A Benchmark Model, IEEE T. Contr. Syst. T., 21, 15, https://doi.org/10.1109/CCA.2013.6662784, 2013. a
openFAST: openFAST Documentation, https://openfast.readthedocs.io/en/main/ (last access: 26 October 2021), 2021. a
Pourmohammad, S. and Fekih, A.: Fault-Tolerant Control of Wind Turbine
Systems – A Review, 2011 IEEE Green Technologies Conference
(IEEE-Green), 14–15 April 2011, 1–6, https://doi.org/10.1109/GREEN.2011.5754880, 2011. a, b
Qiao, W. and Lu, D.: A Survey on Wind Turbine Condition Monitoring
and Fault Diagnosis – Part I: Components and Subsystems, IEEE
T. Ind. Electron., 62, 6536–6545,
https://doi.org/10.1109/TIE.2015.2422112, 2015a. a
Qiao, W. and Lu, D.: A Survey on Wind Turbine Condition Monitoring
and Fault Diagnosis – Part II: Signals and Signal Processing
Methods, IEEE T. Ind. Electron., 62, 6546–6557,
https://doi.org/10.1109/TIE.2015.2422394, 2015b. a
Rotondo, D., Nejjari, F., Puig, V., and Blesa, J.: Fault tolerant control of
the wind turbine benchmark using virtual sensors/actuators, IFAC Proc. Vol., 8, 114–119, https://doi.org/10.3182/20120829-3-MX-2028.00185, 2012. a
Salem, A. A., Abu-Siada, A., Islam, S., AbuSiada, A., and Islam, S.: A Review
of Condition Monitoring Techniques of the Wind Turbines Gearbox
and Rotor, International Journal of Electrical Energy, 2, 4, https://doi.org/10.12720/ijoee.2.1.53-56, 2014. a
Sami, M. and Patton, R.: An FTC approach to wind turbine power maximisation
via T-S fuzzy modelling and control, IFAC Proc. Vol., 8, 349–354,
https://doi.org/10.3182/20120829-3-MX-2028.00131, 2012. a
Sheng, S.: Report on Wind Turbine Subsystem Reliability – A Survey of Various Databases, National Renewable Energy Laboratory, NREL/PR-5000-59111, https://www.nrel.gov/docs/fy13osti/59111.pdf (last access: 24 February 2021), 2013. a
Simani, S. and Castaldi, P.: Adaptive fault-tolerant control design approach
for a wind turbine benchmark, IFAC Proc. Vol., 8, 319–324,
https://doi.org/10.3182/20120829-3-MX-2028.00066, 2012.
a
Sloth, C., Esbensen, T., and Stoustrup, J.: Active and passive fault-tolerant
LPV control of wind turbines, Proceedings of the 2010 American Control
Conference, 30 June–2 July 2010, 4640–4646, https://doi.org/10.1109/ACC.2010.5531061, 2010. a
Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T., and
Ekemb, G.: Wind turbine condition monitoring: State-of-the-art review, new
trends, and future challenges, Energies, 7, 2595–2630,
https://doi.org/10.3390/en7042595, 2014. a
Wilkinson, M., Darnell, B., Van Delft, T., and Harman, K.: Comparison of
methods for wind turbine condition monitoring with SCADA data, IET
Renew. Power Gen., 8, 390–397, https://doi.org/10.1049/iet-rpg.2013.0318,
2014. a
Short summary
Our article proposes an easy-to-integrate wind turbine control module which mitigates wind turbine fault conditions and sends predictive information to the grid operator, all while maximizing power production. This gives the grid operator more time to react to faults with its dispatch decisions, easing the transition between different generators. This study aims to illustrate the controller’s functionality under various types of faults and highlight potential wind turbine and grid benefits.
Our article proposes an easy-to-integrate wind turbine control module which mitigates wind...
Altmetrics
Final-revised paper
Preprint