Parallel Real-time Nonlinear Model Predictive Control of DFIG-based Wind Turbines over All Operating Regions

Document Type : Research Article


Department of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran


Nonlinear model predictive control (NMPC) is a viable solution for control problems in the industry. In this paper, a real-time NMPC approach is proposed for the control of wind turbine (WT) over operating regions. Using wind speed predictions, the NMPC achieves the right compromise between maximizing power and reducing WT fatigue loads while limiting the generator torque activity and the blade pitch angle and smoothing out the electrical power. The control scheme is tested in a simulation environment with a set of standard high turbulence wind profiles and coherent gusts, utilizing complete aeroelastic modeling of the WT and an all-nonlinear model of the doubly fed induction generator (DFIG) over the whole operation region. Besides, the NMPC has been implemented in a parallel Newton-type approach to make it more efficient and implementable. A wide range of simulation scenarios, as well as statistical analysis, were also performed to demonstrate the performance and robustness of the proposed controller against model parameter uncertainties. In addition, finite-time convergence of the controller is guaranteed by employing terminal constraints. The results show 1.7% increase in power extraction, 11% decrease in shaft load, and 12% decrease in tower load while reducing the activity of control inputs and smoothing the generator power.


Main Subjects

  1. Amoco, Statistical review of world energy. [London],[BP Amoco Plc], in, 2018.
  2. Anaya-Lara, N. Jenkins, J.B. Ekanayake, P. Cartwright, M. Hughes, Wind energy generation: modelling and control, John Wiley & Sons, 2011.
  3. Munteanu, A.I. Bratcu, E. CeangĂ, N.-A. Cutululis, Optimal control of wind energy systems: towards a global approach, Springer, 2008.
  4. Kusiak, Z. Zhang, A. Verma, Prediction, operations, and condition monitoring in wind energy, energy, 60 (2013) 1-12.
  5. Bossanyi, Wind turbine control for load reduction, Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 6(3) (2003) 229-244.
  6. Wang, Y. Xing, A. Karuvathil, O. Gaidai, A comparison study of power performance and extreme load effects of large 10-MW offshore wind turbines, IET Renewable Power Generation, (2023).
  7. P. Girsang, J.S. Dhupia, E. Muljadi, M. Singh, L.Y. Pao, Gearbox and drivetrain models to study dynamic effects of modern wind turbines, IEEE Transactions on Industry Applications, 50(6) (2014) 3777-3786.
  8. G. Njiri, D. Söffker, State-of-the-art in wind turbine control: Trends and challenges, Renewable and Sustainable Energy Reviews, 60 (2016) 377-393.
  9. Harris, M. Hand, A. Wright, Lidar for turbine control, National Renewable Energy Laboratory, Golden, CO, Report No. NREL/TP-500-39154, (2006).
  10. Sawant, S. Thakare, A.P. Rao, A.E. Feijóo-Lorenzo, N.D. Bokde, A review on state-of-the-art reviews in wind-turbine-and wind-farm-related topics, Energies, 14(8) (2021) 2041.
  11. Pavese, Wind energy literature survey no. 34, Wind Energy, 18(7) (2015) 1313-1316.
  12. A. Hannan, A.Q. Al-Shetwi, M.S. Mollik, P.J. Ker, M. Mannan, M. Mansor, H.M.K. Al-Masri, T.M.I. Mahlia, Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions, Sustainability, 15(5) (2023) 3986.
  13. G.M. Almihat, M.T. Kahn, Wind Turbines Control Trends and Challenges: An Overview, International Journal of Innovative Research and Scientific Studies, 5(4) (2022) 378-390.
  14. E.B. Aguilar, D.V. Coury, R. Reginatto, R.M. Monaro, Multi-objective PSO applied to PI control of DFIG wind turbine under electrical fault conditions, Electric Power Systems Research, 180 (2020) 106081.
  15. -h. Hur, W.E. Leithead, Model predictive and linear quadratic Gaussian control of a wind turbine, Optimal Control Applications and Methods, 38(1) (2017) 88-111.
  16. F. Nayeh, H. Moradi, G. Vossoughi, Multivariable robust control of a horizontal wind turbine under various operating modes and uncertainties: A comparison on sliding mode and H∞ control, International Journal of Electrical Power & Energy Systems, 115 (2020) 105474.
  17. Rigatos, P. Siano, M. Abbaszadeh, P. Wira, Nonlinear optimal control for wind power generators comprising a multi-mass drivetrain and a DFIG, Journal of the Franklin Institute, 356(5) (2019) 2582-2605.
  18. A. Naik, C.P. Gupta, E. Fernandez, Design and implementation of interval type-2 fuzzy logic-PI based adaptive controller for DFIG based wind energy system, International Journal of Electrical Power & Energy Systems, 115 (2020) 105468.
  19. D. Bianchi, R.S. Sánchez-Peña, M. Guadayol, Gain scheduled control based on high fidelity local wind turbine models, Renewable energy, 37(1) (2012) 233240.
  20. Wu, B. Zhao, J. Mao, B. Wu, F. Yu, Adaptive active fault-tolerant MPPT control for wind power generation system under partial loss of actuator effectiveness, International Journal of Electrical Power & Energy Systems, 105 (2019) 660-670.
  21. D. Bebars, A.A. Eladl, G.M. Abdulsalam, E.A. Badran, Internal electrical fault detection techniques in DFIG-based wind turbines: A review, Protection and Control of Modern Power Systems, 7(1) (2022) 18.
  22. Evangelista, F. Valenciaga, P. Puleston, Active and reactive power control for wind turbine based on a MIMO 2-sliding mode algorithm with variable gains, IEEE Transactions on Energy Conversion, 28(3) (2013) 682-689.
  23. Xiong, P. Li, F. Wu, M. Ma, M.W. Khan, J. Wang, A coordinated high-order sliding mode control of DFIG wind turbine for power optimization and grid synchronization, International Journal of Electrical Power & Energy Systems, 105 (2019) 679-689.
  24. Mousavi, G. Bevan, I.B. Kucukdemiral, A. Fekih, Sliding mode control of wind energy conversion systems: Trends and applications, Renewable and Sustainable Energy Reviews, 167 (2022) 112734.
  25. Bektache, B. Boukhezzar, Nonlinear predictive control of a DFIG-based wind turbine for power capture optimization, International journal of electrical power & Energy systems, 101 (2018) 92-102.
  26. Soliman, O. Malik, D. Westwick, Multiple model MIMO predictive control for variable speed variable pitch wind turbines, in: Proceedings of the 2010 American Control Conference, IEEE, 2010, pp. 2778-2784.
  27. Jiang, T. Zhang, J. Geng, P. Wang, L. Fu, An MPPT Strategy for Wind Turbines Combining Feedback Linearization and Model Predictive Control, Energies, 16(10) (2023) 4244.
  28. P. Pradhan, B. Subudhi, A. Ghosh, A new optimal model predictive control scheme for a wind energy conversion system, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 35(3) (2022) e2976.
  29. Hu, T.K. Chau, X. Zhang, H.H.-C. Iu, T. Fernando, D. Fan, A Novel Adaptive Model Predictive Control Strategy for DFIG Wind Turbine with Parameter Variations in Complex Power Systems, IEEE Transactions on Power Systems, (2022).
  30. Koerber, R. King, Nonlinear model predictive control for wind turbines, Proc. EWEA, (2011).
  31. Schlipf, D.J. Schlipf, M. Kühn, Nonlinear model predictive control of wind turbines using LIDAR, Wind energy, 16(7) (2013) 1107-1129.
  32. R. Sultana, S.K. Sahoo, S. Sukchai, S. Yamuna, D. Venkatesh, A review on state of art development of model predictive control for renewable energy applications, Renewable and sustainable energy reviews, 76 (2017) 391-406.
  33. H. Lio, J. Rossiter, B.L. Jones, A review on applications of model predictive control to wind turbines, in: 2014 Ukacc international conference on control (control), IEEE, 2014, pp. 673-678.
  34. Song, J. Yang, M. Dong, Y.H. Joo, Model predictive control with finite control set for variable-speed wind turbines, Energy, 126 (2017) 564-572.
  35. Darabian, A. Jalilvand, Predictive control strategy to improve stability of DFIG-based wind generation connected to a large-scale power system, International Transactions on Electrical Energy Systems, 27(5) (2017) e2300.
  36. Von Stryk, R. Bulirsch, Direct and indirect methods for trajectory optimization, Annals of operations research, 37 (1992) 357-373.
  37. Boltyanskiy, R.V. Gamkrelidze, Y. Mishchenko, L. Pontryagin, Mathematical theory of optimal processes, (1962).
  38. Deng, T. Ohtsuka, A parallel Newton-type method for nonlinear model predictive control, Automatica, 109 (2019) 108560.
  39. Gros, M. Vukov, M. Diehl, A real-time MHE and NMPC scheme for wind turbine control, in: 52nd IEEE Conference on Decision and Control, IEEE, 2013, pp. 1007-1012.
  40. Hayman, MLife theory manual for version 1.00, National Renewable Energy Laboratory, Golden, CO, 74(75) (2012) 106.
  41. Jonkman, S. Butterfield, W. Musial, G. Scott, Definition of a 5-MW reference wind turbine for offshore system development, National Renewable Energy Lab. (NREL), Golden, CO (United States), 2009.
  42. Jonkman, Turbsim user’s guide v2. 00.00, Natl. Renew. Energy Lab, (2014).
  43. Heier, Grid integration of wind energy: onshore and offshore conversion systems, John Wiley & Sons, 2014.
  44. ADAMS: The multibody dynamic simulation solution, in, 2015.
  45. Jonkman, M.L. Buhl Jr, NWTC information portal (FAST), National Renewable Energy Laboratory, Golden, CO, accessed May, 11 (2015) 2016.
  46. H. Bladed: A design tool for wind turbine performance and loading, in, 2015.
  47. Øye, FLEX5 Simulation Software, DTU Wind Energy.
  48. Boukhezzar, H. Siguerdidjane, Nonlinear control of a variable-speed wind turbine using a two-mass model, IEEE transactions on energy conversion, 26(1) (2010) 149-162.
  49. A. Abdelbaky, X. Liu, D. Jiang, Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines, Renewable Energy, 145 (2020) 981-996.
  50. -h. Hur, Modelling and control of a wind turbine and farm, Energy, 156 (2018) 360-370.
  51. Björnstedt, Integration of non-synchronous generation-frequency dynamics, Lund University, 2012.
  52. Srirattanawichaikul, S. Premrudeepreechacharn, Y. Kumsuwan, A comparative study of vector control strategies for rotor-side converter of DFIG wind energy systems, in: 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE, 2016, pp. 1-6.
  53. Marino, P. Tomei, C.M. Verrelli, Induction motor control design, Springer Science & Business Media, 2010.
  54. A. Bossanyi, The design of closed loop controllers for wind turbines, Wind energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 3(3) (2000) 149-163.
  55. M. Maciejowski, in: Predictive Control with Constraints, Prentice Hall, 2000.
  56. H. Byrd, M.E. Hribar, J. Nocedal, An interior point algorithm for large-scale nonlinear programming, SIAM Journal on Optimization, 9(4) (1999) 877-900.
  57. V. Rakovic, W.S. Levine, Handbook of model predictive control, Springer, 2018.
  58. Faulwasser,           Optimization-based             solutions                to constrained       trajectory-tracking               and          path-following problems, Magdeburg, Universität, Diss., 2012, 2013.
  59. Bossanyi, B. Savini, M. Iribas, M. Hau, B. Fischer, D. Schlipf, T. van Engelen, M. Rossetti, C. Carcangiu, Advanced controller research for multi-MW wind turbines in the UPWIND project, Wind Energy, 15(1) (2012) 119-145.
  60. Turbines, Part 1: Design Requirements, IEC 614001, International Electrotechnical Commission: Geneva, Switzerland, (2005).
  61. M. Kellett, A.R. Teel, Smooth Lyapunov functions and robustness of stability for difference inclusions, Systems & Control Letters, 52(5) (2004) 395-405.