Object-oriented modelling of wind turbines and its application for control design based on nonlinear dynamic inversion

References

• Modelica Association, editor. Modelica - A Unified Object-Oriented Language for Physical Systems Modeling Language Specification Version 3.2,Como.Linköping: Linköping University Press, 2010.
   Google Scholar
• T. Bellmann, Interactive simulations and advanced visualization with modelica, Proceedings of the 7th Modelica Conference, 541–550, 2009.
   Google Scholar
• FMI development group, Functional mock-up interface for model exchange and co-simulation. Technical report, MODELISAR consortium, 2014.
   Google Scholar
• J. Jonkman, S. Butterfield, W. Musial, and G. Scott, Definition of a 5-mw reference wind turbine for offshore system development. Technical report, National Renewable Energy Laboratory, 2009.
   Google Scholar
• J. Tobolar, M. Otter, and T. Bünte, Modelling of vehicle powertrains with the modelica powertrain library, in Systemanalyse in Der Kfz-Antriebstechnik IV, Systemanalyse in der Kfz-Antriebstechnik IV”, Essen, Germany, 2007.
   Google Scholar
• G. Looye, The new DLR flight dynamics library, Proceedings of the 6th Modelica Conference, 193–202, 2008.
   Google Scholar
• A. Heckmann, M. Otter, S. Dietz, and J. Lopez, The DLR Flexible Bodies Library to Model Large Motions of Beams and of Flexible Bodies Exported from Finite Element Programs, The Modelica Association, Linköping University Press, Linköping 2006.
   Google Scholar
• M. Hansen, Aerodynamics of Wind Turbines, Earthscan, Routledge, London, 2008.
   Google Scholar
• O. Wallrapp, Standardization of flexible body modeling in multibody system codes, part I: definition of standart input data, Mech. Struct. Mach. 22 (3) (1994), pp. 283–304. doi:10.1080/08905459408905214
   Web of Science ®Google Scholar
• P. Thomas, X. Gu, R. Samlaus, C. Hillmann, and U. Wihlfahrt, The onewind modelica library for wind turbine simulation with flexible structure - modal reduction method in modelica, Proceedings of the 10th International Modelica Conference, 2014.
   Google Scholar
• A. Craig Hansen and P.J. Moriarty. Aerodyn theory manual, Technical report, National Renewable Energy Laboratory, 2005.
   Google Scholar
• J. Twele and R. Gasch, Windkraftanlagen, Vieweg+Teubner, Wiesbaden, Germany, 2011.
   Google Scholar
• D. Zimmer. Using artificial states in modeling dynamic systems: turning malpractice into good practice, Proceedings of the 5th International Workshop on Equation-Based Object-Oriented Languages and Tools (EOOLT), 2013.
   Google Scholar
• M. Otter, H. Elmqvist, and S. Mattsson, The new modelica multibody library, Proceedings of the 3rd International Modelica Conference, 2003.
   Google Scholar
• F. Dunne, E. Simley, and L. Pao, Lidar wind speed measurement analysis and feed-forward blade pitch control for load mitigation in wind turbines. Technical report, National Renewable Energy Laboratory, 2011.
   Google Scholar
• A. Koerber and R. King, Combined feedback–feedforward control of wind turbines using state-constrained model predictive control, Ieee. Trans. Control. Syst. Technol. 21 (4) (2013), pp. 1117–1128. doi:10.1109/TCST.2013.2260749
   Web of Science ®Google Scholar
• D. Schlipf, T. Fischer, C. Carcangiu, M. Rossetti, and E. Bossanyi. Load analysis of look-ahead collective pitch control using lidar, Proceedings of the 10th German Wind Energy Conference DEWEK, 17-18 Nov 2010. Universität Stuttgart, 2010.
   Google Scholar
• N. Wang and K. Johnson, Lidar-based fx-rls feedforward control for wind turbine load mitigation, Proceedings of the 2011 American Control Conference, 2011.
   Google Scholar
• J.-J.E. Slotine and L. Weiping, Applied Nonlinear Control, Pearson, Upper Saddle River, NJ, 1991.
   Google Scholar
• E. Johnson and A. Calise, Pseudo-Control Hedging: A New Method for Adaptive Control,Workshop on Advances in Guidance and Control Technology, Alabama, USA, 2000.
   Google Scholar
• H. Geng, S. Xiao, W. Yang, and G. Yang. Nonlinear dynamic inversion approach applied to pitch control of wind turbines, Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014.
   Google Scholar
• F. Holzapfel, Nichtlineare adaptive regelung eines unbemannten fluggerätes, Verlag Dr. Hut.Ph. D. Thesis, Technische Universität München, München,  (2004).
   Google Scholar
• T. Lombaerts, G. Looye, Q. Chu, and J. Mulder, Design and simulation of fault tolerant flight control based on a physical approach, Aerospace Sci. Technol. 23 (1) 35th ERF: Progress in Rotorcraft Research (2012), pp. 151–171. doi:10.1016/j.ast.2011.07.004.
   Web of Science ®Google Scholar
• G. Looye, Design of robust autopilot control laws with nonlinear dynamic inversion, Automatisierungstechnik. 49 (2001), pp. 523–531. doi:10.1524/auto.2001.49.12.523
   Google Scholar
• L.Y. Eric Simley, N.K. Pao, B. Jonkman, and R. Frehlich, Lidar wind speed measurements of evolving wind fields, Proc. ASME Wind Energy Symposium, 2012.
   Google Scholar
• L.Y. Fiona Dunne, D.S. Pao, and A.K. Scholbrock, Importance of lidar measurement timing accuracy for wind turbine control, American Control Conference, ACC 2014, Portland, OR, USA, June 4-6, 2014, 3716–3721, 2014.
   Google Scholar
• D. Schlipf and P.-W. Cheng, Adaptive vorsteuerung für windenergieanlagen, Automatisierungstechnik. 61 (5) (2013), pp. 329–338. doi:10.1524/auto.2013.0029
   Web of Science ®Google Scholar
• E. Bossanyi, B. Savini, M. Iribas, M. Hau, B. Fischer, D. Schlipf, T. van Engelen, M. Rossetti, and C.E. Carcangiu, Advanced controller research for multi-mw wind turbines in the upwind project, Wind Energy. 15 (1) (2012), pp. 119–145. doi:10.1002/we.v15.1
   Web of Science ®Google Scholar
• S. Mattsson and G. Söderlind, Index reduction in differential-algebraic equations using dummy derivatives, SIAM J. Scientific Stat. Comput. 14 (1993), pp. 677–692. doi:10.1137/0914043
   Web of Science ®Google Scholar
• M. Otter, H. Elmqvist, and F. Cellier. Modeling of multibody systems with the object-oriented modeling language dymola, Technical report, 1996.
   Google Scholar
• M. Reiner. Modellierung und Steuerung eines strukturelastischen Roboters. PhD thesis, Technische Universität München, 2011.
   Google Scholar
• H. Joos, J. Bals, G. Looye, K. Schnepper, and A. Varga. A multi-objective optimisation based software environment for control systems design. Proc. of 2002 IEEE International Conference on Control Applications and International Symposium on Computer Aided Control Systems Design, CCA/CACSD, 2002.
   Google Scholar
• M. Reiner and D. Zimmer. Nonlinear dynamic inversion control for wind turbine load mitigation based on wind speed measurement. In Proceedings of the 11th International Modelica Conference, 349–357, 2015.
   Google Scholar