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 may)
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 may)
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 may)
Lemmer, F., Lehmann, K., Raach, S., Al, M., Skandali, D., Schlipf, D., u. a. (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 may)
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 may)
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 may)
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 mar)
Schlipf, D., Lemmer, F., & Thomas, F. (2021). Realization of wind field reconstruction for real- time monitoring and LIDAR-assisted control. http://doi.org/10.5281/zenodo.5608684 (Original work published aug)
Boysen, C., Kaldemeyer, C., Sadat, F., Tuschy, I., Witte, F., Bauer, S., & Dahmke, A. (2021). Integration unterirdischer Speichertechnologien in die Energiesystemtransformation am Beispiel des Modellgebietes Schleswig-Holstein - ANGUS II : Schlussbericht zum Verbundvorhaben Teilprojekt Simulation energietechnischer Einzelanlagen. Hochschule Flensburg. Abgerufen von https://www.tib.eu/de/suchen/id/TIBKAT%3A1798315475