Articles | Volume 8, issue 6
https://doi.org/10.5194/wes-8-925-2023
© 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-8-925-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines
Stuttgart Wind Energy (SWE), University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Vasilis Pettas
Stuttgart Wind Energy (SWE), University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Julia Gottschall
Fraunhofer Institute for Wind Energy Systems IWES, Am Seedeich 45, 27572 Bremerhaven, Germany
Po Wen Cheng
Stuttgart Wind Energy (SWE), University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Related authors
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.
Warren Watson, Gerrit Wolken-Möhlmann, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-45, https://doi.org/10.5194/wes-2025-45, 2025
Revised manuscript under review for WES
Short summary
Short summary
In this study, we compare turbulence intensity measurements from two buoy-mounted wind lidars with data from a fixed lidar and a meteorological mast. Turbulence intensity is essential for understanding wind conditions but is often overestimated by floating systems due to wave motion. We applied a physics-based compensation to reduce these effects. Our findings show that motion compensation significantly improves accuracy, making floating lidar systems suitable for offshore wind site assessments.
Farkhondeh (Hanie) Rouholahnejad and Julia Gottschall
Wind Energ. Sci., 10, 143–159, https://doi.org/10.5194/wes-10-143-2025, https://doi.org/10.5194/wes-10-143-2025, 2025
Short summary
Short summary
In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.
Martin Georg Jonietz Alvarez, Warren Watson, and Julia Gottschall
Wind Energ. Sci., 9, 2217–2233, https://doi.org/10.5194/wes-9-2217-2024, https://doi.org/10.5194/wes-9-2217-2024, 2024
Short summary
Short summary
Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the effect of gaps on the estimated long-term wind resource is lower than expected and that data gap filling does not significantly change the outcome. These results indicate a need to reduce current wind data availability requirements for offshore measurement campaigns.
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.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, https://doi.org/10.5194/wes-9-1419-2024, 2024
Short summary
Short summary
A neural observer is used to estimate shear and veer from the operational data of a large wind turbine equipped with blade load sensors. Comparison with independent measurements from a nearby met mast and profiling lidar demonstrate the ability of the
rotor as a sensorconcept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
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.
Hugo Rubio, Daniel Hatfield, Charlotte Bay Hasager, Martin Kühn, and Julia Gottschall
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-11, https://doi.org/10.5194/amt-2024-11, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Unlocking offshore wind farms’ potential demands a precise understanding of available wind resources. Yet, limited in situ data in marine environments call for innovative solutions. This study delves into the world of satellite remote sensing and numerical models, exploring their capabilities and challenges in characterizing offshore wind dynamics. This investigation evaluates these tools against measurements from a floating ship-based lidar, collected through a novel campaign in the Baltic Sea.
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.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
Short summary
Short summary
A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Konstanze Kölle, Tuhfe Göçmen, Irene Eguinoa, Leonardo Andrés Alcayaga Román, Maria Aparicio-Sanchez, Ju Feng, Johan Meyers, Vasilis Pettas, and Ishaan Sood
Wind Energ. Sci., 7, 2181–2200, https://doi.org/10.5194/wes-7-2181-2022, https://doi.org/10.5194/wes-7-2181-2022, 2022
Short summary
Short summary
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
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.
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021, https://doi.org/10.5194/wes-6-841-2021, 2021
Short summary
Short summary
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
Julia Gottschall and Martin Dörenkämper
Wind Energ. Sci., 6, 505–520, https://doi.org/10.5194/wes-6-505-2021, https://doi.org/10.5194/wes-6-505-2021, 2021
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 Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, https://doi.org/10.5194/gmd-13-5079-2020, 2020
Short summary
Short summary
This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Cited articles
Bischoff, O., Wolken-Möhlmann, G., and Cheng, P. W.:
An approach and discussion of a simulation based measurement uncertainty estimation for a floating lidar system, J. Phys. Conf. Ser., 2265, 022077, https://doi.org/10.1088/1742-6596/2265/2/022077, 2022. a
Borraccino, A., Schlipf, D., Haizmann, F., and Wagner, R.:
Wind field reconstruction from nacelle-mounted lidar short-range measurements, Wind Energ. Sci., 2, 269–283, https://doi.org/10.5194/wes-2-269-2017, 2017. a
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
Browning, K. and Wexler, R.:
The determination of kinematic properties of a wind field using Doppler radar, J. Appl. Meteorol. Clim., 7, 105–113, 1968. a
BWIdeol: Floatgen Wind Power going further Offshore,
FLOATGEN,, https://floatgen.eu/ (last access: 27 April 2023), 2019. a
Chen, Y., Yu, W., Guo, F., and Cheng, P. W.:
Adaptive measuring trajectory for scanning lidars: proof of concept, J. Phys. Conf. Ser., 2265, 022099, https://doi.org/10.1088/1742-6596/2265/2/022099, 2022. a
Clifton, A., Clive, P., Gottschall, J., Schlipf, D., Simley, E., Simmons, L., Stein, D., Trabucchi, D., Vasiljevic, N., and Würth, I.:
IEA Wind Task 32: Wind Lidar Identifying and Mitigating Barriers to the Adoption of Wind Lidar, Remote Sens.-Basel, 10, https://doi.org/10.3390/rs10030406, 2018. a
Conti, D., Dimitrov, N., and Peña, A.:
Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements, Wind Energ. Sci., 5, 1129–1154, https://doi.org/10.5194/wes-5-1129-2020, 2020. a, b
Conti, D., Pettas, V., Dimitrov, N., and Peña, A.:
Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals, Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021, 2021. a
Désert, T., Knapp, G., and Aubrun, S.:
Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System, Remote Sens.-Basel, 13, https://doi.org/10.3390/rs13152973, 2021. a
Dimitrov, N., Borraccino, A., Peña, A., Natarajan, A., and Mann, J.:
Wind turbine load validation using lidar-based wind retrievals, Wind Energy, 22, 1512–1533, https://doi.org/10.1002/we.2385, 2019. a
ECN: Centrale Nantes offshore test site, https://sem-rev.ec-nantes.fr/ (last access: 27 April 2023), 2017. a
Fleming, P. A., Scholbrock, A. K., Jehu, A., Davoust, S., Osler, E., Wright, A. D., and Clifton, A.:
Field-test results using a nacelle-mounted lidar for improving wind turbine power capture by reducing yaw misalignment, J. Phys. Conf. Ser., 524, 012002, https://doi.org/10.1088/1742-6596/524/1/012002, 2014. a
Gaertner, E., Rinker, J., Sethuraman L.and Zahle, F., Anderson, B., Barter, G., Abbas, N.and Meng, F., Bortolotti, P.and Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Sheilds, M.and Allen, C., and Viselli, A.:
Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine, Tech. rep., National Renewable Energy Laboratory, Golden, CO, https://www.nrel.gov/docs/fy20osti/75698.pdf (last access: 27 April 2023), 2014. a
Gottschall, J., Wolken-Möhlmann, G., and Lange, B.:
About offshore resource assessment with floating lidars with special respect to turbulence and extreme events, J. Phys. Conf. Ser., 555, 012043, https://doi.org/10.1088/1742-6596/555/1/012043, 2014. a
Gottschall, J., Gribben, B., Stein, D., and Würth, I.:
Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity, WIREs Energy Environ., 6, e250, https://doi.org/10.1002/wene.250, 2017. a
Gräfe, M.: FLIDU v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.7930113, 2023. a
Gräfe, M., Pettas, V., and Cheng, P. W.:
Wind field reconstruction using nacelle based lidar measurements for floating wind turbines, J. Phys. Conf. Ser., 2265, 042022, https://doi.org/10.1088/1742-6596/2265/4/042022, 2022. a, b, c
Gutiérrez-Antuñano, M. A., Tiana-Alsina, J., Salcedo, A., and Rocadenbosch, F.: Estimation of the Motion-Induced Horizontal-Wind-Speed Standard Deviation in an Offshore Doppler Lidar, Remote Sens.-Basel, 10, 12, https://doi.org/10.3390/rs10122037, 2018. a
GWEC 2022: Floating offshore wind – a global opportunity, Report, Global Wind Energy Council, https://gwec.net/wp-content/uploads/2022/03/GWEC-Report-Floating-Offshore-Wind-A-Global-Opportunity.pdf (last access: 27 April 2023), 2022. a
IEC 61400-50-3:2022: IEC61400-50-3 ED1 Use of nacelle-mounted lidars for wind measurements, Standard, IEC – International Electrotechnical Commission, https://webstore.iec.ch/publication/59587 (last access: 7 January 2022), 2022. a
JCGM 100:2008: Uncertainty of measurement Part 3: Guide to the expression of uncertainty in measurement (GUM:1995), Standard, International Organization for Standardization (ISO), https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf/cb0ef43f-baa5-11cf-3f85-4dcd86f77bd6
(last access: 27 April 2023), 2008. a
Jonkman, B. J.: TurbSim User's Guide v2, Tech. rep., National Renewable Energy Laboratory, Golden, CO, https://nwtc.nrel.gov/TurbSim (last access: 27 April 2023), 2014. a
Kelberlau, F. and Mann, J.:
Quantification of motion-induced measurement error on floating lidar systems, Atmos. Meas. Tech., 15, 5323–5341, https://doi.org/10.5194/amt-15-5323-2022, 2022. a
Kelberlau, F., Neshaug, V., Lønseth, L., Bracchi, T., and Mann, J.:
Taking the motion out of floating lidar: Turbulence intensity estimates with a continuous-wave wind lidar, Remote Sens.-Basel, 12, 5, https://doi.org/10.3390/rs12050898, 2020. a
Mahfouz, M. Y., Molins, C., Trubat, P., Hernández, S., Vigara, F., Pegalajar-Jurado, A., Bredmose, H., and Salari, M.:
Response of the International Energy Agency (IEA) Wind 15 MW WindCrete and Activefloat floating wind turbines to wind and second-order waves, Wind Energ. Sci., 6, 867–883, https://doi.org/10.5194/wes-6-867-2021, 2021. a, b, c
MATLAB:
9.9.0.1495850 (R2020b), The MathWorks Inc., Natick, Massachusetts, 2020. a
Meyer, P. J. and Gottschall, J.:
Evaluation of the “fan scan” based on three combined nacelle lidars for advanced wind field characterisation, J. Phys. Conf. Ser., 2265, 022107, https://doi.org/10.1088/1742-6596/2265/2/022107, 2022. a
Özinan, U., Liu, D., Adam, R., Choisnet, T., and Cheng, P. W.:
Power curve measurement of a floating offshore wind turbine with a nacelle-based lidar, J. Phys. Conf. Ser., 2265, 042016, https://doi.org/10.1088/1742-6596/2265/4/042016, 2022.
a, b
Pettas, V., García, F., Kretschmer, M., Rinker, J., Clifton, A., and Cheng, P.: A numerical framework for constrainin synthetic wind fields with lidar measurements for improved load simulations, in: Proceedings of AIAA Scitech 2020 Forum, ARC – Aerospace Research Central, https://doi.org/10.2514/6.2020-0993, 2020. a, b
Pettas, V., Costa, F., and Clifton, A.: SWE-UniStuttgart/ViConDAR: ViConDAR V2.0, Zenodo [code], https://doi.org/10.5281/zenodo.6540049, 2022. a, b
Salcedo-Bosch, A., Rocadenbosch, F., and Sospedra, J.: A Robust Adaptive Unscented Kalman Filter for Floating Doppler Wind-LiDAR Motion Correction, Remote Sens.-Basel, 13, 20, https://doi.org/10.3390/rs13204167, 2021. a
Salcedo-Bosch, A., Rocadenbosch, F., and Sospedra, J.: Enhanced Dual Filter for Floating Wind Lidar Motion Correction: The Impact of Wind and Initial Scan Phase Models, Remote Sens.-Basel, 14, 19, https://doi.org/10.3390/rs14194704, 2022. a
Schlipf, D.:
Lidar-assisted control concepts for wind turbines, PhD thesis, University of Stuttgart, 2016. a
Schlipf, D., Fleming, P., Kapp, S., Scholbrock, A., Haizmann, F., Belen, F., Wright, A., and Cheng, P. W.: Direct Speed Control using LIDAR and turbine data, IEEE, 2208–2213, https://doi.org/10.1109/ACC.2013.6580163, 2013. a
Schlipf, D., Simley, E., Lemmer, F., Pao, L., and Cheng, P. W.:
Collective Pitch Feedforward Control of Floating Wind Turbines Using Lidar, iSOPE-I-15-755, ISOPE, https://doi.org/10.18419/opus-3974, 2015. a, b
Schlipf, D., Koch, M., and Raach, S.:
Modeling Uncertainties of Wind Field Reconstruction Using Lidar, J. Phys. Conf. Ser., 1452, 012088, https://doi.org/10.1088/1742-6596/1452/1/012088, 2020. a
Sommer, K.-D. and Siebert, B. R. L.:
Praxisgerechtes Bestimmen der Messunsicherheit nach GUM (Practical Determination of the Measurement Uncertainty under GUM), TM – Tech. Mess., 71, 52–66, https://doi.org/10.1524/teme.71.2.52.27068, 2004. a, b
Vaisala: Wind Iris Turbine Control, https://www.vaisala.com/sites/default/files/documents/WindIrisTurbineControlBrochure.pdf (last access: 27 April 2023), 2022. a
Veers, P. S.: Three-dimensional wind simulation, OSTI.GOV, https://www.osti.gov/biblio/6633902 (last access: 27 April 2023), 1988. a
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.
Inflow wind field measurements from nacelle-based lidar systems offer great potential for...
Altmetrics
Final-revised paper
Preprint