Thiesen, H., Gloe, A., & Jauch, C. (2021). Grid Frequency Data – WETI. http://doi.org/doi.org/10.17605/OSF.IO/JBK82
Abstract
The presented grid frequency data is part of research activities at the Wind Energy Technology Instiute (WETI) at the Flensburg University of Applied Sciences. The measurement campaign is conducted in Flensburg, Germany. Hence, the grid frequency of the synchronous area of Continental Europe is tracked. A Dewetron 2010 measurement system is used to record and compute the data. The measurement system computes the grid frequency by tracking the grid voltage with a high sampling rate of 50 kHz. Every 164 ms the software fits a sinusoidal curve into the recorded voltage measurement points using the least-square-sums approach. The period of the resulting sinusoidal function is used as a measure for grid frequency.
Mayer, L., Süncksen, M., Reinhold, S., Bertel, S., & Teistler, M. (2021). Training visuospatial skills for medical ultrasound imaging with a desktop-based learning game. In 9th International Conference on Serious Games and Applications for Health (SeGAH 2021). Dubai, United Arab Emirates.
Wohlin, C., Papatheocharous, E., Carlson, J., Petersen, K., egroth, E. A., Axelsson, J., … others,. (2021). Towards evidence-based decision-making for identification and usage of assets in composite software: A research roadmap. Journal of Software: Evolution and Process, 33, e2345.
Jauch, C. (2021). Grid Services and Stress Reduction with a Flywheel in the Rotor of a Wind Turbine. Energies, 14. http://doi.org/10.3390/en14092556
Abstract
Wind power penetration increases in most grids and the sizes of wind turbines increase. This leads to increasingly tough requirements, which are imposed on wind turbines, both from the grid as well as from economics. Some of these partially contradictory requirements can only be satisfied with additional control mechanisms in the wind turbines. In this paper, such a mechanism, i.e., a hydraulic–pneumatic flywheel system in the rotor of a wind turbine, is discussed. This flywheel system supports a wind turbine in providing grid services such as steadying the power infeed, fast frequency response, continuous inertia provision, power system stabilization, and low voltage ride-through. In addition, it can help mitigate the stress on the mechanical structure of a wind turbine, which results from varying operating points, imbalances in the rotor, gravitation that acts on the blades, in-plane vibrations, and emergency braking. The study presented in this paper is based on simulations of a publicly available reference wind turbine. Both the rotor blade design as well as the design of the flywheel system are as previously published. It is discussed how the aforementioned grid services and the stress reduction mechanisms can be combined. Finally, it is concluded that such a flywheel system broadens the range of control mechanisms of a wind turbine substantially, which is beneficial for the grid as well as for the wind turbine itself.
Falkowski-Gilski, P., & Uhl, T. (2020). Study of cellular network quality of the eve of 5G technology on a selected example. Warszawa: Journal Przeglad Telekomunikacyjny. http://doi.org/10.15199/59.2020.7-8.45 (Original work published 2020)
Yu, W., Lemmer, F., Schlipf, D., & Cheng, P. W. (2020). Loop shaping based robust control for floating offshore wind turbines. In Journal of Physics: Conference Series (Bd. 1618, S. 022066). http://doi.org/10.1088/1742-6596/1618/2/022066 (Original work published 2026)
Clifton, A., Schlipf, D., Vasiljevic, N., Gottschall, J., Clive, P., Wüerth, I., … Nygaard, N. (2020). IEA Wind Task 32: Collaborative R\&D Roadmap. http://doi.org/10.5281/zenodo.4030701 (Original work published 2026)
Simley, E., Bortolotti, P., Scholbrock, A., Schlipf, D., & Dykes, K. (2020). IEA Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering. In Journal of Physics: Conference Series (Bd. 1618, S. 042029). http://doi.org/10.1088/1742-6596/1618/4/042029 (Original work published 2026)
Schlipf, D., Guo, F., & Raach, S. (2020). Lidar-based Estimation of Turbulence Intensity for Controller Scheduling. In Journal of Physics: Conference Series (Bd. 1618, S. 032053). http://doi.org/10.1088/1742-6596/1618/3/032053 (Original work published 2026)
Schlipf, D., Lemmer, F., & Raach, S. (2020). Multi-Variable Feedforward Control for Floating Wind Turbines Using Lidar. In International Ocean and Polar Engineering Conference. http://doi.org/10.18419/opus-11067 (Original work published 2026)