Wind lidar technology development and transfer
Clifton, A. ., & Schlipf, D. . (2021). Wind lidar technology development and transfer. http://doi.org/10.5281/zenodo.4817725 (Original work published 2024)
Publikationen
2021
The impact of wind evolution and filter design on lidar-assisted wind turbine control
Guo, F. ., Schlipf, D. ., & Chen, Y. . (2021). The impact of wind evolution and filter design on lidar-assisted wind turbine control. http://doi.org/10.5281/zenodo.4890902 (Original work published 2024)Exportformate
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Assessment of a State-Feedback Controller and Observer in a Floating Wind Turbine Scaled Experiment
Lemmer, F. ., Lehmann, K. ., Raach, S. ., Al, M. ., Skandali, D. ., Schlipf, D. ., … Cheng, P. W. (2021). Assessment of a State-Feedback Controller and Observer in a Floating Wind Turbine Scaled Experiment. http://doi.org/10.5281/zenodo.5004916 (Original work published 2024)Exportformate
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Lidar Wind Preview Quality Estimation for Wind Turbine Control
Guo, F. ., & Schlipf, D. . (2021). Lidar Wind Preview Quality Estimation for Wind Turbine Control. In American Control Conference. New Orleans, LA, USA. http://doi.org/10.23919/ACC50511.2021.9483442 (Original work published 2024)Exportformate
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Smart Lidar Systems for Floating Offshore Wind Turbines
Thomas, F. ., Schlipf, D. ., & Raach, S. . (2021). Smart Lidar Systems for Floating Offshore Wind Turbines. http://doi.org/10.5281/zenodo.5004524 (Original work published 2024)Exportformate
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A Low Computational Framework for Testing Wind Farm Controllers
Novais, F. ., Schlipf, D. ., & Raach, S. . (2021). A Low Computational Framework for Testing Wind Farm Controllers. http://doi.org/10.5281/zenodo.5008772 (Original work published 2024)Exportformate
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Comparison of uncertainties in measurements from cup anemometers and lidar systems
Schlipf, D. ., Guo, F. ., & Chen, Y. . (2021). Comparison of uncertainties in measurements from cup anemometers and lidar systems. http://doi.org/10.5281/zenodo.4890902 (Original work published 2024)Exportformate
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Bayesian neural network model for estimating fatigue loads on wind turbines
Omole, J. A., Schlipf, D. ., Venu, A. ., & Ludde, M. . (2021). Bayesian neural network model for estimating fatigue loads on wind turbines. http://doi.org/10.5281/zenodo.4923193 (Original work published 2024)Exportformate
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The Smart Lidar Concept - New Opportunities for the Lidar Community
Schlipf, D. ., Guo, F. ., Raach, S. ., & Zhu, H. . (2021). The Smart Lidar Concept - New Opportunities for the Lidar Community. http://doi.org/10.5281/zenodo.4627168 (Original work published 2024)Exportformate
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Parameterization of wind evolution using lidar
Chen, Y. ., Schlipf, D. ., & Cheng, P. W. (2021). Parameterization of wind evolution using lidar. Wind Energy Science, 6, 61–91. http://doi.org/10.5194/wes-6-61-2021 (Original work published 2024)Exportformate
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