Skip to main content

Variable speed wind turbine power control: A comparison between multiple MPPT based methods

Abstract

Reducing the renewable energy costs is necessary for the competition with the fossil energies and control strategies have great impact on the efficiency of wind machines. In the wind turbine industry, a practical approach is to maximize the energy capture of a wind machine by optimizing the power coefficient in the under-rated situations. In this paper, with the main objective of maximizing the energy capture in the second region, four different control strategies are compared in the presence of uncertainties. The proposed control methods are compared based on their power capture and robustness against probable uncertainties in the structural and environmental parameters. A two-mass mechanical model is used and verified by FAST simulations. The generator torque is considered as the control input and the control objective is to track the rotor angular velocity in order to achieve the optimum value of power coefficient. First, a common sliding mode controller is reviewed, then a modern PI-Neural Network controller (PI-NN) studied, and then a backstepping controller is designed; finally, a hybrid H and feedback linearization controller (H-FL) is introduced. While all controllers demonstrate an appropriate performance in terms of the tracking and efficiency indices, backstepping method shows better efficiency, Sliding mode and PI-NN are similar in terms of power efficiency and H-FL controller showed the least efficiency. On the other hand, the backstepping controller suffer from the high fluctuations in control signal and H-FL controller has a considerably smoother torque. The proposed controllers can also come up with the disturbances so that they can tolerate the structural and environmental parameters discrepancy to some extent. The backstepping and H-FL controllers showed similarly the best robustness, but sliding mode controller shows less robustness. The robustness of the PI-NN performance lies between the sliding mode and backstepping controller.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

References

  1. Menezes EJN, Araújo AM, da Silva NSB (2018) A review on wind turbine control and its asssociated methods. J Clean Prod 174:945–953

    Google Scholar 

  2. Njiri JG, Söffker D (2016) State-of-the-art in wind turbine control: trends and challenges. Renew Sustain Energy Rev 60:377–393

    Google Scholar 

  3. Jabbari Asl H, Yoon J (2016) Power capture optimization of variable-speed wind turbines using an output feedback controller. Renew Energy. 86:517–525

    Google Scholar 

  4. Kumar D, Chatterjee K (2016) A review of conventional and advanced MPPT algorithms for wind energy systems. Renew Sustain Energy Rev 55:957–970

    Google Scholar 

  5. Moradi H, Vossoughi G (2015) Robust control of the variable speed wind turbines in the presence of uncertainties: a comparison between H∞ and PID controllers. Energy 90:1508–1521

    Google Scholar 

  6. Ghafouri M, Karaagac U, Karimi H, Jensen S, Mahseredjian J, Faried SO (2017) An lqr controller for damping of subsynchronous interaction in DFIG-based wind farms. IEEE Trans Power Syst 32:4934–4942

    Google Scholar 

  7. Q. Li, T. Gao, D.W. Gao, X. Wang, (2017) Adaptive LQR control with Kalman filter for the variable-speed wind turbine in Region II. In: 2017 Power Symposium (NAPS), North American, pp 1–6

  8. R.M. Imran, D.M.A. Hussain, Z. Chen, (2014) LQG controller design for pitch regulated variable speed wind turbine. In: 2014 IEEE International Energy Conference (ENERGYCON), pp 101–105

  9. Houtzager I, Van Wingerden JW, Verhaegen M (2013) Rejection of periodic wind disturbances on a smart rotor test section using lifted repetitive control. IEEE Trans Control Syst Technol 21:347–359

    Google Scholar 

  10. R. Faraji Nayeh, (2015) Robust multivariable control of electro-mechanical system in horizontal wind turbines under off-design conditions (Master’s Thesis), Sharif University of Technology, Tehran, Iran (In Persian)

  11. Zhu M, Liu J, Lin Z, Meng H (2016) Mixed H2/H∞ pitch control of wind turbine generator system based on global exact linearization and regional pole placement. Int J Mach Learn Cybern 7:921–930

    Google Scholar 

  12. A. ur Rehman, O. Khan, N. Ali, M. Pervaiz, (2018) Nonlinear robust control of a variable speed-wind turbine. In: 2018 International Conference On Engineering and Emerging Technologies (ICEET), pp 1–6

  13. Abdeddaim S, Betka A (2013) Optimal tracking and robust power control of the DFIG wind turbine. Int J Electr Power Energy Syst 49:234–242

    Google Scholar 

  14. Meghni B, Dib D, Azar AT (2017) A second-order sliding mode and fuzzy logic control to optimal energy management in wind turbine with battery storage. Neural Comput Appl 28:1417–1434

    Google Scholar 

  15. Golnary F, Moradi H (2018) Design and comparison of quasi continuous sliding mode control with feedback linearization for a large scale wind turbine with wind speed estimation. Renew Energy 127:495–508

    Google Scholar 

  16. Kenné G, Fotso AS, Lamnabhi-Lagarrigue F (2017) A new adaptive control strategy for a class of nonlinear system using RBF neuro-sliding-mode technique: application to SEIG wind turbine control system. Int J Control 90:855–872

    MathSciNet  MATH  Google Scholar 

  17. Pao L, Johnson K (2011) Control of wind turbines. Approaches, challenges, and recent developments. IEEE Control Syst Mag 58:44–62

    MathSciNet  MATH  Google Scholar 

  18. Rotea MA (2017) Logarithmic power feedback for extremum seeking control of wind turbines. IFAC-Pap OnLine 50:4504–4509

    Google Scholar 

  19. Xiao Y, Li Y, Rotea MA (2018) CART3 field tests for wind turbine region-2 operation with extremum seeking controllers. IEEE Trans Control Syst Technol 4:1744–1752

    Google Scholar 

  20. Brahmi J, Krichen L, Ouali A (2009) A comparative study between three sensorless control strategies for PMSG in wind energy conversion system. Appl Energy 86:1565–1573

    Google Scholar 

  21. Kusiak A, Li W, Song Z (2010) Dynamic control of wind turbines. Renew Energy 35:456–463

    Google Scholar 

  22. Spencer MD, Stol KA, Unsworth CP, Cater JE, Norris SE (2013) Model predictive control of a wind turbine using short-term wind field predictions. Wind Energy 16:417–434

    Google Scholar 

  23. Jain A, Schildbach G, Fagiano L, Morari M (2015) On the design and tuning of linear model predictive control for wind turbines. Renew Energy 80:664–673

    Google Scholar 

  24. Lin Z, Chen Z, Liu J, Wu Q (2019) Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy. Appl Energy 236:307–317

    Google Scholar 

  25. Obukhov S, Ibrahim A, Zaki Diab AA, Al-Sumaiti AS, Aboelsaud R (2020) Optimal performance of dynamic particle swarm optimization based maximum power trackers for stand-alone PV system under partial shading conditions. IEEE Access 8:20770–20785

    Google Scholar 

  26. Ibrahim A, Aboelsaud R, Obukhov S (2019) Improved particle swarm optimization for global maximum power point tracking of partially shaded PV array. Electr Eng 101:443–455

    Google Scholar 

  27. Song D, Yang J, Cai Z, Dong M, Su M, Wang Y (2017) Wind estimation with a non-standard extended Kalman filter and its application on maximum power extraction for variable speed wind turbines. Appl Energy 190:670–685

    Google Scholar 

  28. Aissou S, Rekioua D, Mezzai N, Rekioua T, Bacha S (2015) Modeling and control of hybrid photovoltaic wind power system with battery storage. Energy Convers Manag 89:615–625

    Google Scholar 

  29. Muyeen SM, Ali MH, Takahashi R, Murata T, Tamura J, Tomaki Y, Sakahara A, Sasano E (2007) Comparative study on transient stability analysis of wind turbine generator system using different drive train models. IET Renew Power Gener 1:131–141

    Google Scholar 

  30. Zhao H, Wu Q, Guo Q, Sun H, Xue Y (2015) Distributed model predictive control of a wind farm for optimal active power control part II: implementation with clustering-based piece-wise affine wind turbine model. IEEE Trans Sustain Energy 6:840–849

    Google Scholar 

  31. Pan Y, Du P, Xue H, Lam HK (2020) Singularity-free fixed-time fuzzy control for robotic systems with user-defined performance. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.2999746

    Article  Google Scholar 

  32. Liang H, Liu G, Zhang H, Huang T (2020) Neural-network-based event-triggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3003950

    Article  Google Scholar 

  33. Malcolm DJ (2006) WindPACT turbine rotor design study. National Renewable Energy Lab (NREL), Golden, CO, United States

    Google Scholar 

  34. Boukhezzar B, Siguerdidjane H (2011) Nonlinear control of a variable-speed wind turbine using a two-mass model. IEEE Trans Energy Convers 26:149–162

    Google Scholar 

  35. B. Boukhezzar, H. Siguerdidjane, (2005) Nonlinear control of variable speed wind turbines for power regulation. In: Proceedings of 2005 IEEE Conference on Control Applications, CCA 2005, pp 114–119

  36. Iyasere AE, Salah M, Wagner J (2007) Technical report: nonlinear robust control to maximize energy capture in a variable speed wind turbine in a variable speed wind Turbine. College of Engineering and Science Control and Robotics (CRB) Clemson University, Clemson

    MATH  Google Scholar 

  37. Beltran B, Ahmed-Ali T, Benbouzid MEH (2008) Sliding mode power control of variable-speed wind energy conversion systems. IEEE Trans Energy Convers 23:551–558

    Google Scholar 

  38. Saravanakumar R, Jena D (2015) Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine. Int J Electr Power Energy Syst 69:421–429

    Google Scholar 

  39. Yin X, Lin Y, Li W, Liu H, Gu Y (2015) Adaptive sliding mode back-stepping pitch angle control of a variable-displacement pump controlled pitch system for wind turbines. ISA Trans 58:629–634

    Google Scholar 

  40. Poultangari I, Shahnazi R, Sheikhan M (2012) RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm. ISA Trans 51:641–648

    Google Scholar 

  41. Ata R (2015) Artificial neural networks applications in wind energy systems: a review. Renew Sustain Energy Rev 49:534–562

    Google Scholar 

  42. Assareh E, Biglari M (2015) A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm. Renew Sustain Energy Rev 51:1023–1037

    Google Scholar 

  43. Kumar MBH, Saravanan B, Sanjeevikumar P, Blaabjerg F (2018) Review on control techniques and methodologies for maximum power extraction from wind energy systems. IET Renew Power Gener 12:1609–1622

    Google Scholar 

  44. Marugán AP, Márquez FPG, Perez JMP, Ruiz-Hernández D (2018) A survey of artificial neural network in wind energy systems. Appl Energy 228:1822–1836

    Google Scholar 

  45. F.D. Foresee, M.T. Hagan, (1997) Gauss-Newton approximation to Bayesian learning. In: Proceedings of International Conference on Neural Networks (ICNN’97). vol 3, pp 1930–1935

  46. Picard A, Davis RS, Gläser M, Fujii K (2008) Revised formula for the density of moist air. Metrologia 45:149

    Google Scholar 

  47. TL Grigorie, L Dinca, J Corcau, O Grigorie, (2010) The density altitude, influence factors and evaluation. In: 6th WSEAS International Conference on Dynamical Systems and Control

  48. Skogestad S, Postlethwaite I (2007) Multivariable feedback control: analysis and design. Wiley, New York

    MATH  Google Scholar 

  49. Jonkman JM, Buhl ML Jr (2005) Technical report: FAST user’s guide, (TP-500-38230). National Renewable Energy Lab (NREL), Golden, CO, United States

    Google Scholar 

  50. Manjock A (2005) Evaluation report: design codes FAST and ADAMS for load calculations of onshore wind turbines, Germanischer Lloyd WindEnergie GmbH, Rept 72042. Hamburg, Germany

    Google Scholar 

  51. Buhl ML Jr (2004) WT_Perf user’s guide. National Renewable Energy Laboratory, Golden, CO

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the “Research Office of Sharif University of Technology, Tehran, Iran” for supporting this research through the grant program # QB950944.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hamed Moradi.

Ethics declarations

Conflict of interest

The author declares that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nouriani, A., Moradi, H. Variable speed wind turbine power control: A comparison between multiple MPPT based methods. Int. J. Dynam. Control 10, 654–667 (2022). https://doi.org/10.1007/s40435-021-00784-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-021-00784-6

Keywords