Articles | Volume 8, issue 2
https://doi.org/10.5194/wes-8-149-2023
© Author(s) 2023. 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-8-149-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Feb 2023
Research article |
| 09 Feb 2023
Evaluation of lidar-assisted wind turbine control under various turbulence characteristics
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
Feng Guo and David Schlipf
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-9, https://doi.org/10.5194/wes-2023-9, 2023
Revised manuscript under review for WES
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) optimization of 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, (c) assessing the benefits of the two control strategies using realistic offshore turbulence spectral characteristics.
Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 7, 539–558, https://doi.org/10.5194/wes-7-539-2022, https://doi.org/10.5194/wes-7-539-2022, 2022
Short summary
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.
Feng Guo and David Schlipf
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-9, https://doi.org/10.5194/wes-2023-9, 2023
Revised manuscript under review for WES
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) optimization of 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, (c) assessing the benefits of the two control strategies using realistic offshore turbulence spectral characteristics.
Moritz Johann Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-11, https://doi.org/10.5194/wes-2023-11, 2023
Revised manuscript accepted for WES
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 behaviour of the floating foundations. Therefore, 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.
Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 7, 539–558, https://doi.org/10.5194/wes-7-539-2022, https://doi.org/10.5194/wes-7-539-2022, 2022
Short summary
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.
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.
Martin Hofsäß, Dominique Bergmann, Jan Denzel, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-81, https://doi.org/10.5194/wes-2019-81, 2019
Revised manuscript not accepted
Short summary
Short summary
We needed a way to measure wind vectors and turbulence in complex, hard-to-access terrain. We equipped a model helicopter with a standard 3-D ultrasonic anemometer. Due to the hovering capabilities, stationary point measurements are possible. The first measurements were made in flat terrain. A 100 m high stationary wind measuring mast served as reference. The results were investigated in the time domain as well as in the frequency domain.
Steffen Raach, Bart Doekemeijer, Sjoerd Boersma, Jan-Willem van Wingerden, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-54, https://doi.org/10.5194/wes-2019-54, 2019
Publication in WES not foreseen
Short summary
Short summary
The presented work combines two control approaches of wake redirection control, feedforward wake redirection and feedback wake redirction. In our previous investigatins the lidar-assisted feedback control was studied and the advantages and disadvantages were discussed. The optimal yaw angles for the wind turbines are precomputed, the feedback takes care of uncertainties and disturbances. The concept is demonstrated in a high fidelity simulation model.
Jennifer Annoni, Paul Fleming, Andrew Scholbrock, Jason Roadman, Scott Dana, Christiane Adcock, Fernando Porte-Agel, Steffen Raach, Florian Haizmann, and David Schlipf
Wind Energ. Sci., 3, 819–831, https://doi.org/10.5194/wes-3-819-2018, https://doi.org/10.5194/wes-3-819-2018, 2018
Short summary
Short summary
This paper addresses the modeling aspect of wind farm control. To implement successful wind farm controls, a suitable model has to be used that captures the relevant physics. This paper addresses three different wake models that can be used for controls and compares these models with lidar field data from a utility-scale turbine.
Kolja Müller and Po Wen Cheng
Wind Energ. Sci., 3, 149–162, https://doi.org/10.5194/wes-3-149-2018, https://doi.org/10.5194/wes-3-149-2018, 2018
Short summary
Short summary
An efficient and accurate Monte Carlo approach is presented to assess the lifetime fatigue loading on a floating offshore wind turbine accurately. This is typically challenging in simulation effort due to the many different combinations of relevant environmental conditions which need to be considered. The applied method uses quasi-random Sobol sequences and shows promising performance with respect to convergence and accuracy.
Antoine Borraccino, David Schlipf, Florian Haizmann, and Rozenn Wagner
Wind Energ. Sci., 2, 269–283, https://doi.org/10.5194/wes-2-269-2017, https://doi.org/10.5194/wes-2-269-2017, 2017
Short summary
Short summary
The paper describes an innovative methodology to extract useful wind field estimates from remote sensing instruments – called lidars – mounted on the nacelle of a wind turbine. We used lidar measurements at multiple distances upstream and close to the wind turbine rotor in order to retrieve free-stream wind characteristics. The methodology and the obtained results open new paths to assess the power performance of wind turbines, which is essential to ensure financially viable wind farm projects.
Steffen Raach, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 2, 257–267, https://doi.org/10.5194/wes-2-257-2017, https://doi.org/10.5194/wes-2-257-2017, 2017
Short summary
Short summary
This work provides a possible solution to closed-loop flow control in a wind farm.
The remote sensing technology, lidar, which is a laser-based measurement system, is used to obtain wind speed information behind a wind turbine. The measurements are processed using a model-based approach to estimate position information of the wake. The information is then used in a controller to redirect the wake to the desired position. Altogether, the concept aims to increase the power output of a wind farm.
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.
G. A. M. van Kuik, J. Peinke, R. Nijssen, D. Lekou, J. Mann, J. N. Sørensen, C. Ferreira, J. W. van Wingerden, D. Schlipf, P. Gebraad, H. Polinder, A. Abrahamsen, G. J. W. van Bussel, J. D. Sørensen, P. Tavner, C. L. Bottasso, M. Muskulus, D. Matha, H. J. Lindeboom, S. Degraer, O. Kramer, S. Lehnhoff, M. Sonnenschein, P. E. Sørensen, R. W. Künneke, P. E. Morthorst, and K. Skytte
Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, https://doi.org/10.5194/wes-1-1-2016, 2016
Related subject area
Thematic area: Dynamics and control | Topic: Wind turbine control
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
FarmConners wind farm flow control benchmark – Part 1: Blind test results
Demonstration of a fault impact reduction control module for wind turbines
Lidar-assisted model predictive control of wind turbine fatigue via online rainflow counting considering stress history
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.
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.
Benjamin Anderson and Edward Baring-Gould
Wind Energ. Sci., 7, 1753–1769, https://doi.org/10.5194/wes-7-1753-2022, https://doi.org/10.5194/wes-7-1753-2022, 2022
Short summary
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.
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
Abbas, N. J., Zalkind, D. S., Pao, L., and Wright, A.: A reference open-source controller for fixed and floating offshore wind turbines, Wind Energ. Sci., 7, 53–73, https://doi.org/10.5194/wes-7-53-2022, 2022. a, b, c, d
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
Chen, Y., Schlipf, D., and Cheng, P. W.: Parameterization of wind evolution using lidar, Wind Energ. Sci., 6, 61–91, https://doi.org/10.5194/wes-6-61-2021, 2021. a, b, c
Chen, Z. and Stol, K.: An assessment of the effectiveness of individual pitch
control on upscaled wind turbines, J. Phys.-Conf. Ser.,
524, 012045, https://doi.org/10.1088/1742-6596/524/1/012045,
2014. a
Cheynet, E., Jakobsen, J. B., and Obhrai, C.: Spectral characteristics of
surface-layer turbulence in the North Sea, Energ. Proced., 137, 414–427,
https://doi.org/10.1016/j.egypro.2017.10.366, 2017. 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
Davoust, S. and von Terzi, D.: Analysis of wind coherence in the longitudinal
direction using turbine mounted lidar, J. Phys.-Conf. Ser.,
753, 072005, https://doi.org/10.1088/1742-6596/753/7/072005, 2016. a
DNV-GL: Bladed theory manual: version 4.8, Tech. rep., Garrad Hassan &
Partners Ltd., Bristol, UK, 2016. a
Dong, L., Lio, W. H., and Simley, E.: On turbulence models and lidar measurements for wind turbine control, Wind Energ. Sci., 6, 1491–1500, https://doi.org/10.5194/wes-6-1491-2021, 2021. a, b, c
Dunne, F., Schlipf, D., Pao, L., Wright, A., Jonkman, B., Kelley, N., and
Simley, E.: Comparison of two independent lidar-based pitch control designs, in: 50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Nashville, Tennessee, January 2012,
https://www.osti.gov/biblio/1047948 (last access: 1 Februrary 2023), p. 1151, 2012. a
fengguoFUAS: MSCA-LIKE/OpenFAST3.0_Lidarsim: OpenFAST3.0_Lidarsim (OpenFAST3.0_Lidarsim_v1), Zenodo [code], https://doi.org/10.5281/zenodo.7594971, 2023a. a
fengguoFUAS: MSCA-LIKE/4D-Mann-Turbulence-Generator: 4D-Mann-Turbulence-Generator (4D_MannTurbulence_v1), Zenodo [code], https://doi.org/10.5281/zenodo.7594951, 2023b. a
fengguoFUAS: MSCA-LIKE/Baseline-Lidar-assisted-Controller: Baseline-Lidar-assisted-Controller (Baseline-Lidar-assisted-Controllerv_1), Zenodo [code], https://doi.org/10.5281/zenodo.7594961, 2023c. a
Hunt, J. C. and Carruthers, D. J.: Rapid distortion theory and the
“problems” of turbulence, J. Fluid Mech., 212, 497–532,
https://doi.org/10.1017/S0022112090002075, 1990. a
Jones, B. L., Lio, W., and Rossiter, J.: Overcoming fundamental limitations of
wind turbine individual blade pitch control with inflow sensors, Wind Energy,
21, 922–936, https://doi.org/10.1002/we.2205, 2018. a
Jonkman, B. J.: TurbSim user's guide: Version 1.50, Tech. rep., National
Renewable Energy Lab. (NREL), Golden, CO (United States),
https://doi.org/10.2172/965520, 2009. a
Jonkman, J. and Buhl, M. L.: FAST User's Guide, Tech. Rep. EL-500-38230,
NREL, https://doi.org/10.2172/15020796, 2005. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW
reference wind turbine for offshore system development, Tech. rep., National
Renewable Energy Lab. (NREL), Golden, CO (United States),
https://doi.org/10.2172/947422, 2009. a, b, c
Julier, S. J. and Uhlmann, J. K.: Unscented filtering and nonlinear estimation,
P. IEEE, 92, 401–422, https://doi.org/10.1109/JPROC.2003.823141, 2004. 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
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, IEEE, 3673–3679, https://doi.org/10.1109/ACC.2013.6580400, 2013. a
Lee, K., Shin, H., and Bak, Y.: Control of Power Electronic Converters and
Systems, Academic Press, 392 pp., https://doi.org/10.1016/C2015-0-02427-3, 2018. a
Mann, J.: Wind field simulation, Probabilist. Eng. Mech., 13,
269–282, https://doi.org/10.1016/S0266-8920(97)00036-2, 1998. a, b, c
Mann, J., Cariou, J.-P. C., Parmentier, R. M., Wagner, R., Lindelöw, P.,
Sjöholm, M., and Enevoldsen, K.: Comparison of 3D turbulence measurements
using three staring wind lidars and a sonic anemometer, Meteorol.
Z., 18, 135–140, https://doi.org/10.1127/0941-2948/2009/0370, 2009. a
Matsuishi, M. and Endo, T.: Fatigue of metals subjected to varying stress,
Japan Society of Mechanical Engineers, Fukuoka, Japan, 68, 37–40, 1968. a
Mirzaei, M. and Mann, J.: Lidar configurations for wind turbine control,
J. Phys.-Conf. Ser., 753, 032019,
https://doi.org/10.1088/1742-6596/753/3/032019, 2016. a, b, c
NREL: OpenFAST Documentation, Tech. Rep. Release v3.3.0, National
Renewable Energy Laboratory,
https://openfast.readthedocs.io/en/main/ (last access: 1 January 2023), 2022. a
Nybø, A., Nielsen, F. G., Reuder, J., Churchfield, M. J., and Godvik, M.:
Evaluation of different wind fields for the investigation of the dynamic
response of offshore wind turbines, Wind Energy, 23, 1810–1830,
https://doi.org/10.1002/we.2518, 2020. 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
Peña, A., Hasager, C. B., Lange, J., Anger, J., Badger, M., and Bingöl,
F.: Remote Sensing for Wind Energy, Tech. Rep. DTU Wind
Energy-E-Report-0029(EN), DTU Wind Energy, Roskilde, Denmark,
https://orbit.dtu.dk/files/55501125/Remote_Sensing_for_Wind_Energy.pdf (last access: 1 February 2023),
2013. 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,
2013a. a, b, c
Schlipf, D., Schlipf, D. J., and Kühn, M.: Nonlinear model predictive control
of wind turbines using LIDAR, Wind Energy, 16, 1107–1129,
https://doi.org/10.1002/we.1533, 2013b. a
Schlipf, D., Fürst, H., Raach, S., and Haizmann, F.: Systems Engineering for
Lidar-Assisted Control: A Sequential Approach, J. Phys.-Conf.
Ser., 1102, 012014, https://doi.org/10.1088/1742-6596/1102/1/012014,
2018a. 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,
2018b. a
Schlipf, D., Lemmer, F., and Raach, S.: Multi-variable feedforward control for
floating wind turbines using lidar, in: The 30th International Ocean and
Polar Engineering Conference, OnePetro,
Virtual, 11–16 October 2020,
https://doi.org/10.18419/opus-11067, 2020. a
Shan, M.: Load Reducing Control for Wind Turbines: Load Estimation and Higher
Level Controller Tuning based on Disturbance Spectra and Linear Models, PhD
thesis, Kassel, Universität Kassel, Fachbereich Elektrotechnik/Informatik,
https://kobra.uni-kassel.de/handle/123456789/2017050852519 (last access: 1 February 2023),
2017. a
Simley, E. and Pao, L.: Reducing LIDAR wind speed measurement error with
optimal filtering, in: 2013 American Control Conference, Washington, DC, USA, 17–19 June 2013, 621–627,
https://doi.org/10.1109/ACC.2013.6579906, 2013.
a, b, c
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, b, c, d
Stammler, M., Schwack, F., Bader, N., Reuter, A., and Poll, G.: Friction torque of wind-turbine pitch bearings – comparison of experimental results with available models, Wind Energ. Sci., 3, 97–105, https://doi.org/10.5194/wes-3-97-2018, 2018. a
von Kármán, T.: Progress in the statistical theory of turbulence, P. Natl. Acad. Sci. USA, 34, 530,
https://doi.org/10.1073/pnas.34.11.530, 1948. a
Welch, P.: The use of fast Fourier transform for the estimation of power
spectra: a method based on time averaging over short, modified periodograms,
IEEE Trans. Audio, 15, 70–73,
https://doi.org/10.1109/TAU.1967.1161901, 1967. a, b
Wiener, N.: Extrapolation, interpolation, and smoothing of stationary
time series: with engineering applications, vol. 8, MIT press Cambridge, MA, ISBN 9780262730051,
1964. a
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.
The benefits of lidar-assisted control are evaluated using both the Mann model and Kaimal...
