Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-539-2022
© 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-7-539-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Mar 2022
Research article |
| 11 Mar 2022
| 11 Mar 2022
Four-dimensional wind field generation for the aeroelastic simulation of wind turbines with lidars
Stuttgart Wind Energy (SWE), Institute of Aircraft Design, University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Wind Energy Technology Institute, Flensburg University of Applied Sciences, Kanzleistraße 91–93, 24943 Flensburg, Germany
David Schlipf
Wind Energy Technology Institute, Flensburg University of Applied Sciences, Kanzleistraße 91–93, 24943 Flensburg, Germany
Po Wen Cheng
Stuttgart Wind Energy (SWE), Institute of Aircraft Design, University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Related authors
Yiyin Chen, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 6, 61–91, https://doi.org/10.5194/wes-6-61-2021, https://doi.org/10.5194/wes-6-61-2021, 2021
Short summary
Short summary
Wind evolution is currently of high interest, mainly due to the development of lidar-assisted wind turbine control (LAC). Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into 3D simulations to provide a more realistic simulation environment for LAC. Motivated by these factors, we investigate the potential of Gaussian process regression in the parameterization of a two-parameter wind evolution model using data of two nacelle-mounted lidars.
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024, https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Short summary
This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
Mohammad Youssef Mahfouz, Ericka Lozon, Matthew Hall, and Po Wen Cheng
Wind Energ. Sci., 9, 1595–1615, https://doi.org/10.5194/wes-9-1595-2024, https://doi.org/10.5194/wes-9-1595-2024, 2024
Short summary
Short summary
As climate change increasingly impacts our daily lives, a transition towards cleaner energy is needed. With all the growth in floating offshore wind and the planned floating wind farms (FWFs) in the next few years, we urgently need new techniques and methodologies to accommodate the differences between the fixed bottom and FWFs. This paper presents a novel methodology to decrease aerodynamic losses inside an FWF by passively relocating the downwind floating wind turbines out of the wakes.
Fiona Dominique Lüdecke, Martin Schmid, and Po Wen Cheng
Wind Energ. Sci., 9, 1527–1545, https://doi.org/10.5194/wes-9-1527-2024, https://doi.org/10.5194/wes-9-1527-2024, 2024
Short summary
Short summary
Large direct-drive wind turbines, with a multi-megawatt power rating, face design challenges. Moving towards a more system-oriented design approach could potentially reduce mass and costs. Exploiting the full design space, though, may invoke interaction mechanisms, which have been neglected in the past. Based on coupled simulations, this work derives a better understanding of the electro-mechanical interaction mechanisms and identifies potential for design relevance.
Qi Pan, Dexing Liu, Feng Guo, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-44, https://doi.org/10.5194/wes-2024-44, 2024
Preprint withdrawn
Short summary
Short summary
The floating wind market is striving to scale up from a handful of prototypes to gigawatt-scale capacity, despite facing barriers of high costs in the deep-sea deployment. Shared mooring is promising in reducing material costs. This paper introduces a comprehensive design methodology for reliable shared mooring line configurations, and reveals their potential for cost-saving and power enhancement. These findings contribute to achieving cost-effective solutions for floating wind farms.
Wei Yu, Sheng Tao Zhou, Frank Lemmer, and Po Wen Cheng
Wind Energ. Sci., 9, 1053–1068, https://doi.org/10.5194/wes-9-1053-2024, https://doi.org/10.5194/wes-9-1053-2024, 2024
Short summary
Short summary
Integrating a tuned liquid multi-column damping (TLMCD) into a floating offshore wind turbine (FOWT) is challenging. The synergy between the TLMCD, the turbine controller, and substructure dynamics affects the FOWT's performance and cost. A control co-design optimization framework is developed to optimize the substructure, the TLMCD, and the blade pitch controller simultaneously. The results show that the optimization can significantly enhance FOWT system performance.
Christian W. Schulz, Stefan Netzband, Umut Özinan, Po Wen Cheng, and Moustafa Abdel-Maksoud
Wind Energ. Sci., 9, 665–695, https://doi.org/10.5194/wes-9-665-2024, https://doi.org/10.5194/wes-9-665-2024, 2024
Short summary
Short summary
Understanding the underlying physical phenomena of the aerodynamics of floating offshore wind turbines (FOWTs) is crucial for successful simulations. No consensus has been reached in the research community on which unsteady aerodynamic phenomena are relevant and how much they can influence the loads acting on a FOWT. This work contributes to the understanding and characterisation of such unsteady phenomena using a novel experimental approach and comprehensive numerical investigations.
Zhaoyu Zhang, Feng Guo, David Schlipf, Paolo Schito, and Alberto Zasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-162, https://doi.org/10.5194/wes-2023-162, 2024
Preprint withdrawn
Short summary
Short summary
This paper aims to analyse the uncertainty in wind direction estimation of LIDAR and to improve the estimation accuracy. Findings demonstrate that this LIDAR estimation method is insufficient to supervise the turbine yaw control system in terms of both accuracy and timeliness. Future research should apply more advanced wind flow models to explore more accurate wind field reconstruction methods.
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.
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.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
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.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
Short summary
Short summary
This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Matthias Kretschmer, Jason Jonkman, Vasilis Pettas, and Po Wen Cheng
Wind Energ. Sci., 6, 1247–1262, https://doi.org/10.5194/wes-6-1247-2021, https://doi.org/10.5194/wes-6-1247-2021, 2021
Short summary
Short summary
We perform a validation of the new simulation tool FAST.Farm for the prediction of power output and structural loads in single wake conditions with respect to measurement data from the offshore wind farm alpha ventus. With a new wake-added turbulence functionality added to FAST.Farm, good agreement between simulations and measurements is achieved for the considered quantities. We hereby give insights into load characteristics of an offshore wind turbine subjected to single wake conditions.
Yiyin Chen, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 6, 61–91, https://doi.org/10.5194/wes-6-61-2021, https://doi.org/10.5194/wes-6-61-2021, 2021
Short summary
Short summary
Wind evolution is currently of high interest, mainly due to the development of lidar-assisted wind turbine control (LAC). Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into 3D simulations to provide a more realistic simulation environment for LAC. Motivated by these factors, we investigate the potential of Gaussian process regression in the parameterization of a two-parameter wind evolution model using data of two nacelle-mounted lidars.
Cited articles
Banakh, V. and Smalikho, I.: Estimation of the turbulence energy dissipation
rate from the pulsed Doppler lidar data, J. Atmos. Ocean.
Tech., 10, 957–965, 1994. a
Bartlett, M. S.: Smoothing periodograms from time-series with continuous
spectra, Nature, 161, 686–687, https://doi.org/10.1038/161686a0, 1948. a
Bos, R.: Extreme gusts and their role in wind turbine design, Dissertation,
Delft University of Technology,
https://doi.org/10.4233/uuid:d6097e3a-1cdd-4845-a71c-90f469d28b7a,
2017. a, b
Bossanyi, E.: Un-freezing the turbulence: Application to LiDAR-assisted wind
turbine control, IET Renew. Power Gen., 7, 321–329,
https://doi.org/10.1049/iet-rpg.2012.0260, 2013. a, b
Bossanyi, E. A., Kumar, A., and Hugues-Salas, O.: Wind turbine control
applications of turbine-mounted LIDAR, J. Phys.-Conf. Ser.,
555, 012011, https://doi.org/10.1088/1742-6596/555/1/012011, 2014. a, b
Chen, Y. and Cheng, P. W.: Comparison of different machine learning algorithms
for prediction of wind evolution, J. Phys.-Conf. Ser.,
1618, 062060, https://doi.org/10.1088/1742-6596/1618/6/062060, 2020. a
Chen, Y. and Guo, F.: evoTurb v1.0.0, Zenodo [code],
https://doi.org/10.5281/zenodo.5028595, 2021. a
Davenport, A. G.: The spectrum of horizontal gustiness near the ground in high
winds, Q. J. Roy. Meteor. Soc., 87, 194–211,
https://doi.org/10.1002/qj.49708737208, 1961. a
de Maré, M. and Mann, J.: On the space-time structure of sheared
turbulence, Bound.-Lay. Meteorol., 160, 453–474,
https://doi.org/10.1007/s10546-016-0143-z, 2016. a, b, c
DNV, G.: DNV GL-ST-0437: Loads and Site Conditions for Wind Turbines, Standard,
DNV GL, Høvik, November, https://www.dnv.com/energy/standards-guidelines/dnv-st-0437-loads-and-site-conditions-for-wind-turbines.html (last access: 2 March 2022) 2016. a
DNV-GL: Bladed theory manual: version 4.8, Tech. rep., Garrad Hassan &
Partners Ltd., Bristol, UK, 2016. a
Fujii, T. and Fukuchi, T.: Laser remote sensing, vol. 97 of Optical
engineering, CRC Press, Boca Raton, 2005. a
Guo, F. and Schlipf, D.: Lidar Wind Preview Quality Estimation for Wind Turbine
Control, in: 2021 American Control Conference (ACC), New Orleans, LA, USA, 25–28 May 2021, 552–557,
https://doi.org/10.23919/ACC50511.2021.9483442, 2021. a
Held, D. P. and Mann, J.: Lidar estimation of rotor-effective wind speed – an experimental comparison, Wind Energ. Sci., 4, 421–438, https://doi.org/10.5194/wes-4-421-2019, 2019. a, b, c, d
Higham, N. J.: Functions of matrices: Theory and computation, Society for
Industrial and Applied Mathematics, Philadelphia, https://doi.org/10.1137/1.9780898717778, 2008. a
Kaimal, J. C., Wyngaard, J. C., Izumi, Y., and Coté, O. R.: Spectral
characteristics of surface-layer turbulence, Q. J. Roy.
Meteor. Soc., 98, 563–589, https://doi.org/10.1002/qj.49709841707, 1972. a, b, c, d
Kolmogorov, A. N.: Dissipation of energy in locally isotropic turbulence, Dokl.
Akad. Nauk SSSR, 32, 19–21, 1941 (in Russian). a
Laks, J., Simley, E., and Pao, L.: A spectral model for evaluating the effect
of wind evolution on wind turbine preview control, in: 2013 American Control
Conference, Washington, DC, USA, 17–19 June 2013, 3673–3679, IEEE, https://doi.org/10.1109/ACC.2013.6580400, 2013. a, b, c
Mann, J., Cariou, J.-P., Courtney, M. S., Parmentier, R., Mikkelsen, T.,
Wagner, R., Lindelöw, P., Sjöholm, M., and Enevoldsen, K.: Comparison of 3D
turbulence measurements using three staring wind lidars and a sonic
anemometer, IOP Conf. Ser. Earth and Environmental Science, 1,
012012, https://doi.org/10.1088/1755-1315/1/1/012012, 2008. a
Moriarty, P. J. and Butterfield, S. B.: Wind turbine modeling overview for
control engineers, in: 2009 American Control Conference, 2090–2095,
IEEE, https://doi.org/10.1109/ACC.2009.5160521, 2009. a
Panofsky, H. A. and Mizuno, T.: Horizontal coherence and Pasquill's beta,
Bound.-Lay. Meteorol., 9, 247–256, https://doi.org/10.1007/BF00230769, 1975. a
Peña, A., Mann, J., and Dimitrov, N.: Turbulence characterization from a forward-looking nacelle lidar, Wind Energ. Sci., 2, 133–152, https://doi.org/10.5194/wes-2-133-2017, 2017. a, b, c
Pielke, R. A. and Panofsky, H. A.: Turbulence characteristics along several
towers, Bound.-Lay. Meteorol., 1, 115–130, https://doi.org/10.1007/BF00185733,
1970. a, b, c
Pope, S. B.: Turbulent Flows, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9780511840531, 2000. a
Schacke, K.: On the Kronecker product, Master's thesis, University of Waterloo,
2004. a
Schlipf, D., Cheng, P. W., and Mann, J.: Model of the Correlation between Lidar
Systems and Wind Turbines for Lidar-Assisted Control, J. Atmos.
Ocean. Tech., 30, 2233–2240, https://doi.org/10.1175/JTECH-D-13-00077.1,
2013. a, b
Schlipf, D., Haizmann, F., Cosack, N., Siebers, T., and Cheng, P. W.: Detection
of Wind Evolution and Lidar Trajectory Optimization for Lidar-Assisted Wind
Turbine Control, Meteorol. Z., 24, 565–579,
https://doi.org/10.1127/metz/2015/0634, 2015. a, b
Schlipf, D., Hille, N., Raach, S., Scholbrock, A., and Simley, E.: IEA Wind
Task 32: Best Practices for the Certification of Lidar-Assisted Control
Applications, J. Phys.-Conf. Ser., 1102,
012010, https://doi.org/10.1088/1742-6596/1102/1/012010, 2018. a
Schlipf, D., Guo, F., and Raach, S.: Lidar-based Estimation of Turbulence
Intensity for Controller Scheduling, J. Phys.-Conf. Ser.,
1618, 032053, https://doi.org/10.1088/1742-6596/1618/3/032053, 2020. a
Simley, E., Fürst, H., Haizmann, F., and Schlipf, D.: Optimizing Lidars for
wind turbine control applications – Results from the IEA wind task 32
Workshop, Remote Sensing, 10, 863, https://doi.org/10.3390/rs10060863, 2018. a
Taylor, G. I.: The spectrum of turbulence, P. Roy. Soc.
A-Mat. Phy., 164, 476–490, 1938. a
Thresher, R. W., Holley, W. E., Smith, C. E., Jafarey, N., and Lin, S.-R.:
Modeling the response of wind turbines to atmospheric turbulence, Tech. Rep.
RL0/2227-81/2, Mechanical Engineering Department, Oregon State University,
Corvallis, Oregon, 1981. a
Weitkamp, C. (Ed.): Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere,
102, Springer-Verlag, New York, https://doi.org/10.1007/b106786, 2005. a
Short summary
Lidar-assisted control of wind turbines requires a wind field generator capable of simulating wind evolution. Out of this need, we extend the Veers method for 3D wind field generation to 4D and propose a two-step Cholesky decomposition approach. Based on this, we develop a 4D wind field generator – evoTurb – coupled with TurbSim and Mann turbulence generator. We further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide practical suggestions.
Lidar-assisted control of wind turbines requires a wind field generator capable of simulating...
Altmetrics
Final-revised paper
Preprint