Skip to main content
Log in

Variable Speed Wind Turbine Control Using the Homotopy Perturbation Method

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing-Green Technology Aims and scope Submit manuscript

Abstract

In this study, we present a method to obtain optimal control of the variable-speed fixed-pitch wind turbine using the homotopy perturbation method (HPM). In general, the optimal control problem for nonlinear systems should solve the Hamilton–Jacobi–Bellman (HJB) equation. The partial differential HJB equations that arise in optimal control problem, give closed-loop control law and it is difficult to obtain an exact solution of them for nonlinear systems. The main objective of this work is to employ the homotopy perturbation method to solve the HJB equation for a two-mass model of a wind turbine to capture the maximum power from the wind in below-rated wind speed. By applying this strategy, we obtained an approximate solution of the HJB equation for a two-mass model of the wind turbine with high accuracy. In the simulation section, we compare the results of the proposed HPM strategy with the nonlinear static state feedback control (NSSFE) approach. The presented results confirm that the HPM controller produces more electrical power while minimizing low-speed shaft oscillations by improving dynamic characteristics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bhandari, B., Lee, K. T., Lee, G. Y., Cho, Y. M., & Ahn, S. H. (2015). Optimization of hybrid renewable energy power systems: A review. International Journal of Precision Engineering and Manufacturing-Green Technology, 2(1), 99–112.

    Article  Google Scholar 

  2. Kavya, M., & Jayalalitha, S. (2021). A Novel Shift and Search (S&S) Algorithm for Tracking Maximum Power in PV Systems: An Approach to Increase Efficiency. International Journal of Precision Engineering and Manufacturing-Green Technology, 8(6), 1699–1710.

    Article  Google Scholar 

  3. Bhandari, B., Poudel, S. R., Lee, K. T., & Ahn, S. H. (2014). Mathematical modeling of hybrid renewable energy system: A review on small hydro-solar-wind power generation. International Journal of Precision Engineering and Manufacturing-Green Technology, 1(2), 157–173.

    Article  Google Scholar 

  4. Kim, K., Kim, H., Paek, I., Kim, H. G., & Son, J. (2019). Field validation of demanded power point tracking control algorithm for medium-capacity wind turbine. International Journal of Precision Engineering and Manufacturing-Green Technology, 6(5), 875–881.

    Article  Google Scholar 

  5. Boukhezzar, B., & Siguerdidjane, H. (2010). Nonlinear control of a variable-speed wind turbine using a two-mass model. IEEE Transactions on Energy Conversion, 26(1), 149–162.

    Article  Google Scholar 

  6. Bektache, A., & Boukhezzar, B. (2018). Nonlinear predictive control of a DFIG-based wind turbine for power capture optimization. International Journal of Electrical Power & Energy Systems, 101, 92–102.

    Article  Google Scholar 

  7. Yin, X. X., Lin, Y. G., Li, W., Gu, Y. J., Lei, P. F., & Liu, H. W. (2015). Sliding mode voltage control strategy for capturing maximum wind energy based on fuzzy logic control. International Journal of Electrical Power & Energy Systems, 70, 45–51.

    Article  Google Scholar 

  8. Lewis, F. L., Vrabie, D., & Syrmos, V. L. (2012). Optimal control. John Wiley & Sons.

    Book  MATH  Google Scholar 

  9. Mangasarian, O. L. (1966). Sufficient conditions for the optimal control of nonlinear systems. SIAM Journal on control, 4(1), 139–152.

    Article  MATH  Google Scholar 

  10. Zhang, Y., Li, S., & Liao, L. (2019). Near-optimal control of nonlinear dynamical systems: A brief survey. Annual Reviews in Control, 47, 71–80.

    Article  Google Scholar 

  11. Chen, X., & Chen, X. (2017). An iterative method for optimal feedback control and generalized HJB equation. IEEE/CAA Journal of Automatica Sinica, 5(5), 999–1006.

    Article  Google Scholar 

  12. Zhu, J. (2017). A feedback optimal control by Hamilton–Jacobi–Bellman equation. European Journal of Control, 37, 70–74.

    Article  MATH  Google Scholar 

  13. Fakharian, A., & Hamidi Beheshti, M. T. (2010). Solving linear and nonlinear optimal control problem using modified a domian decomposition method. Journal of Computer & Robotics, 1(1), 79–86.

    Google Scholar 

  14. Shirazian, M., & Effati, S. (2012). Solving a class of nonlinear optimal control problems via He’s variational iteration method. International Journal of Control, Automation and Systems, 10(2), 249–256.

    Article  Google Scholar 

  15. Kafash, B., Delavarkhalafi, A., & Karbassi, S. M. (2013). Application of variational iteration method for Hamilton–Jacobi–Bellman equations. Applied Mathematical Modelling, 37(6), 3917–3928.

    Article  MATH  Google Scholar 

  16. Nik, H. S., Effati, S., & Shirazian, M. (2012). An approximate-analytical solution for the Hamilton–Jacobi–Bellman equation via homotopy perturbation method. Applied Mathematical Modelling, 36(11), 5614–5623.

    Article  MATH  Google Scholar 

  17. Effati, S., Saberi Nik, H., & Shirazian, M. (2013). An improvement to the homotopy perturbation method for solving the Hamilton–Jacobi–Bellman equation. IMA Journal of Mathematical Control and Information, 30(4), 487–506.

    Article  MATH  Google Scholar 

  18. Ganjefar, S., & Rezaei, S. (2016). Modified homotopy perturbation method for optimal control problems using the Padé approximant. Applied Mathematical Modelling, 40(15–16), 7062–7081.

    Article  MATH  Google Scholar 

  19. He, J. H. (1999). Homotopy perturbation technique. Computer Methods in Applied Mechanics and Engineering, 178(3–4), 257–262.

    Article  MATH  Google Scholar 

  20. He, J. H. (2003). Homotopy perturbation method: A new nonlinear analytical technique. Applied Mathematics and Computation, 135(1), 73–79.

    Article  MATH  Google Scholar 

  21. Sakar, M. G., Uludag, F., & Erdogan, F. (2016). Numerical solution of time-fractional nonlinear PDEs with proportional delays by homotopy perturbation method. Applied Mathematical Modelling, 40(13–14), 6639–6649.

    Article  MATH  Google Scholar 

  22. Hussain, S., Shah, A., Ayub, S., & Ullah, A. (2019). An approximate analytical solution of the Allen-Cahn equation using homotopy perturbation method and homotopy analysis method. Heliyon, 5(12), e03060.

    Article  Google Scholar 

  23. Wu, Y., & He, J. H. (2018). Homotopy perturbation method for nonlinear oscillators with coordinate dependent mass. Results Phys, 10, 270–271.

    Article  Google Scholar 

  24. Hur, S. H. (2018). Modelling and control of a wind turbine and farm. Energy, 156, 360–370.

    Article  Google Scholar 

  25. Hall, J. F., & Chen, D. (2013). Dynamic optimization of drivetrain gear ratio to maximize wind turbine power generation—part 1: System model and control framework. Journal of Dynamic Systems, Measurement, and Control, 135(1), 011016.

    Article  Google Scholar 

  26. Lim, C. W. (2017). Design and manufacture of small-scale wind turbine simulator to emulate torque response of MW wind turbine. International Journal of Precision Engineering and Manufacturing-Green Technology, 4(4), 409–418.

    Article  Google Scholar 

  27. Ghorbani, A. (2009). Beyond a domian polynomials: He polynomials. Chaos, Solitons & Fractals, 39(3), 1486–1492.

    Article  MATH  Google Scholar 

  28. Kirk, D. E. (2004). Optimal control theory: An introduction. Courier Corporation.

    Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soheil Ganjefar.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shalbafian, A., Ganjefar, S. Variable Speed Wind Turbine Control Using the Homotopy Perturbation Method. Int. J. of Precis. Eng. and Manuf.-Green Tech. 10, 141–150 (2023). https://doi.org/10.1007/s40684-022-00422-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40684-022-00422-2

Keywords

Navigation