Takagi-Sugeno based model reference control for wind turbine systems in frequency containment scenarios
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Johannes Brunner
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Horst Schulte
Abstract
In this paper, a model-reference control scheme for wind turbine systems in the Takagi-Sugeno framework is proposed. This work deals with the model reference control approach to provide wind turbines’ fast frequency response (FFR) in fluctuating wind conditions. The proposed scheme is applied to a generic wind turbine model for the partial and full-load region. The controller coefficients are calculated using a set of proposed Linear Matrix Inequality (LMI) conditions. The design process is described in detail, including a controller performance in the wind turbine’s partial and full load region.
Zusammenfassung
In dieser Arbeit wird ein Modellreferenz-Regelungskonzept für Windturbinensysteme im Rahmen des Takagi-Sugeno-Ansatzes vorgestellt. Ziel ist es, durch modellreferente Regelung eine schnelle Frequenzantwort (Fast Frequency Response, FFR) der Windturbinen unter variablen Windbedingungen sicherzustellen. Das Konzept wird auf ein generisches Windturbinenmodell angewendet und sowohl im Teillast- als auch im Volllastbetrieb untersucht. Die Reglerparameter werden auf Basis vorgeschlagener Linear-Matrix-Ungleichungs-Bedingungen (LMI) bestimmt. Der Entwurfsprozess sowie die erzielten Regelungsergebnisse in den unterschiedlichen Betriebsbereichen werden ausführlich beschrieben.
About the authors
Johannes Brunner is a Ph.D. student with the Control Engineering Group at University of Applied Sciences Berlin (HTW). He received his B.Sc. and M.Sc. degrees in Renewable Energy Systems from HTW Berlin in 2022 and 2024, respectively. Upon completion of his master’s degree, he was honored as the top graduate of his class. His research interests include modeling and control of nonlinear systems, Takagi-Sugeno fuzzy systems, singular systems, and input derivative systems, with applications in wind turbine control, inverter control and power systems. He is also interested in dynamic virtual power plants for distribution grid applications.
Horst Schulte is Professor of Control Engineering at the Department of Engineering – Energy and Information, University of Applied Sciences Berlin (HTW). Together with Prof. Brandtstädter, he leads the Control Engineering Group and is the academic head of the European Master’s Program Dynamics of Renewables-based Power Systems. In addition, he serves as Chairman of the Federation of German Windpower and Other Renewable Energies (FGW e.V.). His research interests include computational intelligence in automatic control, modeling and stability analysis of nonlinear dynamic systems, robust and fault-tolerant control, and their applications in power systems. Specific application domains comprise wind and photovoltaic power plants, power electronics, active distribution grids, and dynamic virtual power plants.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
References
[1] N. Hatziargyriou, et al.., “Definition and classification of power system stability – Revisited & extended,” IEEE Trans. Power Syst., vol. 36, no. 4, pp. 3271–3281, 2021.10.1109/TPWRS.2020.3041774Search in Google Scholar
[2] J. Brunner, J. Fortmann, and H. Schulte, “Model reference control for wind turbine systems in full load region based on Takagi-Sugeno fuzzy systems,” in 2024 28th International Conference on Methods and Models in Automation and Robotics (MMAR), Poland, IEEE, 2024, pp. 53–58.10.1109/MMAR62187.2024.10680768Search in Google Scholar
[3] H. Schulte, J. Fortmann, and J. Brunner, Formal Grid Integration of Photovoltaic and Wind Power using the Model Reference Control Approach, Helsinki, 2024.10.1049/icp.2024.3920Search in Google Scholar
[4] J. Aho, P. Fleming, and L. Y. Pao, “Active power control of wind turbines for ancillary services: a comparison of pitch and torque control methodologies,” in 2016 American Control Conference (ACC), Boston Marriott Copley Place, Boston, MA, USA, 2016.10.1109/ACC.2016.7525114Search in Google Scholar
[5] C. Galinos, A. M. Urban, and W. H. Lio, “Optimised de-rated wind turbine response and loading through extended controller gain-scheduling,” J. Phys.: Conf. Ser., vol. 1222, no. 012020, pp. 1–8, 2019. https://doi.org/10.1088/1742-6596/1222/1/012020.Search in Google Scholar
[6] M. Shan, W. Shan, F. Welck, and D. Duckwitz, “Design and laboratory test of black-start control mode for wind turbines,” Wind Energy, vol. 23, no. 3, pp. 763–778, 2020. https://doi.org/10.1002/we.2457.Search in Google Scholar
[7] B. Ibáñez, F. Inthamoussou, and H. De Battista, “Wind turbine load analysis of a full range lpv controller,” Renewable Energy, vol. 145, pp. 2741–2753, 2020. https://doi.org/10.1016/j.renene.2019.08.016.Search in Google Scholar
[8] F. A. Inthamoussou, H. D. Battista, and R. J. Mantz, “LPV-based active power control of wind turbines covering the complete wind speed range,” Renewable Energy, vol. 99, pp. 996–1007, 2016. https://doi.org/10.1016/j.renene.2016.07.064.Search in Google Scholar
[9] F. Pöschke, J. Fortmann, and H. Schulte, “Nonlinear wind turbine controller for variable power generation in full load region,” in American Control Conference, Sheraton Hotel, Seattle, USA, 2017, pp. 1395–1400.10.23919/ACC.2017.7963148Search in Google Scholar
[10] F. Pöschke, E. Gauterin, M. Kühn, J. Fortmann, and H. Schulte, “Load mitigation and power tracking capability for wind turbines using liner matrix Inequality-based control design,” Wind Energy, vol. 23, no. 9, pp. 1792–1809, 2020. https://doi.org/10.1002/we.2516.Search in Google Scholar
[11] K. Kim, H.-G. Kim, C. Jin Kim, I. Paek, C. L. Bottasso, and F. Campagnolo, “Design and validation of demanded power point tracking control algorithm of wind turbine,” Int. J. Precis. Eng. Manuf. Green Technol., vol. 5, no. 3, pp. 387–400, 2018. https://doi.org/10.1007/s40684-018-0041-6.Search in Google Scholar
[12] F. Pöschke, V. Petrović, F. Berger, L. N. M. Hölling, M. Kühn, and H. Schulte, “Model-based wind turbine control design with power tracking capability: a wind-tunnel validation,” Control Eng. Pract., vol. 120, no. 105014, pp. 1–13, 2022.10.1016/j.conengprac.2021.105014Search in Google Scholar
[13] H. Pishbahar, F. Blaabjerg, and H. Saboori, “Emerging grid-forming power converters for renewable energy and storage resources integration – a review,” Sustain. Energy Technol. Assess., vol. 60, 2023, Art. no. 103538. https://doi.org/10.1016/j.seta.2023.103538.Search in Google Scholar
[14] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control,” IEEE Trans. Syst. Man Cybern., vol. 15, no. 1, pp. 116–132, 1985.10.1109/TSMC.1985.6313399Search in Google Scholar
[15] F. Pöschke, Takagi-Sugeno Methods with Application to Wind Power Systems, Ph.D. thesis, Medien- und Informationszentrum, Leuphana Universität Lüneburg, 2022.Search in Google Scholar
[16] K. Tanaka and H. O. Wang, Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach, New York, John Wiley & Sons, Inc, 2001.10.1002/0471224596Search in Google Scholar
[17] J. G. VanAntwerp and R. D. Braatz, “A tutorial on linear and bilinear matrix inequalities,” J. Process Control, vol. 10, no. 4, pp. 363–385, 2000. https://doi.org/10.1016/s0959-1524(99)00056-6.Search in Google Scholar
[18] C. Scherer and S. Weiland, Linear Matrix Inequalities in Control, Stuttgart, 2015.Search in Google Scholar
[19] M. Chilali and P. Gahinet, “H∞ design with pole placement constraints: An LMI approach,” IEEE Trans. Autom. Control, vol. 41, no. 3, pp. 358–367, 1996. https://doi.org/10.1109/9.486637.Search in Google Scholar
[20] R. Gasch and J. Twele, Windkraftanlagen, Berlin, Springer, Vieweg + Teubner, 2016.10.1007/978-3-658-12361-1Search in Google Scholar
[21] J. Jonkman, S. Butterfield, W. Musial, and G. Scott, Definition of a 5-MW Reference Wind Turbine for Offshore System Development, Golden, Colorado, National Renewable Energy Laboratory, Technical Report, NREL/TP-500-38060, 2009.10.2172/947422Search in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Selected contributions from the workshops “Computational Intelligence” in 2023 and 2024
- Methods
- Nonlinear system categorization for structural data mining with state space models
- Incorporation of structural properties of the response surface into oblique model trees
- Takagi-Sugeno based model reference control for wind turbine systems in frequency containment scenarios
- On autoregressive deep learning models for day-ahead wind power forecasts with irregular shutdowns due to redispatching
- Applications
- Efficiently determining the effect of data set size on autoencoder-based metamodels for structural design optimization
- Kalibriermodellerstellung und Merkmalsselektion für die mikromagnetische Materialcharakterisierung mittels maschineller Lernverfahren
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Articles in the same Issue
- Frontmatter
- Editorial
- Selected contributions from the workshops “Computational Intelligence” in 2023 and 2024
- Methods
- Nonlinear system categorization for structural data mining with state space models
- Incorporation of structural properties of the response surface into oblique model trees
- Takagi-Sugeno based model reference control for wind turbine systems in frequency containment scenarios
- On autoregressive deep learning models for day-ahead wind power forecasts with irregular shutdowns due to redispatching
- Applications
- Efficiently determining the effect of data set size on autoencoder-based metamodels for structural design optimization
- Kalibriermodellerstellung und Merkmalsselektion für die mikromagnetische Materialcharakterisierung mittels maschineller Lernverfahren
- Investigating quality inconsistencies in the ultra-high performance concrete manufacturing process using a search-space constrained non-dominated sorting genetic algorithm II
- EAP4EMSIG – enhancing event-driven microscopy for microfluidic single-cell analysis
