Skip to main content

Advertisement

Log in

Auto Disturbance Rejection Control Strategy of Wind Turbine Permanent Magnet Direct Drive Individual Variable Pitch System Under Load Excitation

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

The wind turbine blades have complex stress states, and the load has the characteristics of time-varying and uncertainty. Aiming at the interference problem caused by complex and variable load characteristics to the individual pitch control system, combined with the variable-pitch load characteristics, based on the permanent magnet synchronous motor (PMSM) vector control strategy and LADRC (Auto Disturbance Rejection Speed Controller), an individual pitch speed controller is designed. Linear expansion state observer is built. The control algorithm is deduced and the relationship between each control parameter is analyzed to improve the anti-interference ability of the system to the abrupt load of the pitch. On this basis, the system position tracking controller is built. Finally, a vector control simulation model was built based on MATLB/Simulink, and a speed control simulation was carried out. The simulation results show that LADRC has a strong anti-load disturbance capability, and has better anti-disturbance resistance than PID speed control, and the position controller based on this has a better position tracking effect. At the same time, the effectiveness of the designed independent pitch speed controller has also been verified through experiments. It is of great significance for improving the conversion efficiency of wind energy.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Liu W, Ren H (2020) A novel wind turbine health condition monitoring method based on common features distribution adaptation. Int J Energy Res 44(11):8681–8688

    Article  Google Scholar 

  2. Liu WY, Tang BP, Han JG, Lu XN, Hu NN, He ZZ (2015) The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renew Sustain Energy Rev 44:466–472

    Article  Google Scholar 

  3. Magar KT, Balas MJ, Frost S (2015) Direct adaptive control for individual blade pitch control of wind turbines for load reduction. J Intell Mater Syst Struct 26(12):1564–1572

    Article  Google Scholar 

  4. van Solingen E, Fleming PA, Scholbrock A, van Wingerden JW (2016) Field testing of linear individual pitch control on the two-bladed controls advanced research turbine. Wind Energy 19(3):421–436

    Article  Google Scholar 

  5. Jin X, Wang Y, Li L (2016) Dynamics loads optimization analysis of wind turbine based on LQG independent pitch control. Proc CSEE 36(22):6164–6170

    Google Scholar 

  6. Zhang Y, Chen Z, Hu W, Cheng M (2014) Flicker mitigation by individual pitch control of variable speed wind turbines With DFIG. IEEE Transac Energy Conver 29(1):20–28

    Article  Google Scholar 

  7. Liu X, Yu W, Yang L and Ma Q (2017) Research on variable-pitch control strategy of wind turbine based on the nonlinear PID. In: 2017 Chinese Automation Congress (CAC) (pp. 299-303). IEEE.

  8. Xie B, Wang S, Wang Y, Zhao Z, Xiu J (2015) Magnetically induced rotor vibration in dual-stator permanent magnet motors. J Sound Vibr 347:184–199

    Article  Google Scholar 

  9. Zhou Y, Li H, Zhang H (2018) Model-free deadbeat predictive current control of a surface-mounted permanent magnet synchronous motor drive system. J Power Electr 18(1):103–115

    Google Scholar 

  10. Usop Z, Sarhan AAD, Mardi NA, Wahab MNA (2015) Measuring of positioning, circularity and static errors of a CNC Vertical Machining Centre for validating the machining accuracy. Measurement 61:39–50

    Article  Google Scholar 

  11. Zi B, Sun H, Zhang D (2017) Design, analysis and control of a winding hybrid-driven cable parallel manipulator. Robot Comput-Integr Manufac 48:196–208

    Article  Google Scholar 

  12. Chen X, Wei H, Deng T, He Z, Zhao S (2018) Investigation of electromechanical coupling torsional vibration and stability in a high-speed permanent magnet synchronous motor driven system. Appl Math Modell 64:235–248

    Article  MathSciNet  Google Scholar 

  13. Nerg J, Rilla M, Ruuskanen V, Pyrhoenen J, Ruotsalainen S (2014) Direct-driven interior magnet permanent-magnet synchronous motors for a full electric sports car. IEEE Transac Industr Electr 61(8):4286–4294

    Article  Google Scholar 

  14. Ren J-J, Liu Y-C, Wang N, Liu S-Y (2015) Sensorless control of ship propulsion interior permanent magnet synchronous motor based on a new sliding mode observer. ISA Transac 54:15–26

    Article  Google Scholar 

  15. Lu E, Li W, Yang X, Xu S (2017) Composite sliding mode control of a permanent magnet direct-driven system for a mining scraper conveyor. IEEE Access 5:22399–22408

    Article  Google Scholar 

  16. Sheng L, Li W, Wang Y, Fan M, Yang X (2017) Sensorless control of a shearer short-range cutting interior permanent magnet synchronous motor based on a new sliding mode observer. IEEE Access 5:18439–18450

    Article  Google Scholar 

  17. Wallmark O, Harnefors L, Carlson O (2007) Control algorithms for a fault-tolerant PMSM drive. IEEE Transac Industr Electron 54(4):1973–1980

    Article  Google Scholar 

  18. Bossanyi EA (2003) Individual blade pitch control for load reduction. Wind Energy 6(2):119–128

    Article  Google Scholar 

  19. Sandquist F, Moe G, Anaya-Lara O (2012) Individual pitch control of horizontal axis wind turbines. J Offshore Mechan Arctic Eng 134(3):031901

    Article  Google Scholar 

  20. Liu H, Tang Q, Chi Y, Zhang Z, Yuan X (2016) Vibration reduction strategy for wind turbine based on individual pitch control and torque damping control. Int Transac Electr Energy Syst 26(10):2230–2243

    Article  Google Scholar 

  21. Ren Y, Li L, Brindley J, Lin J (2016) Nonlinear PI control for variable pitch wind turbine. Control Eng Pract 50:84–94

    Article  Google Scholar 

  22. Tan Luong V, Thanh Hai N, Lee D-C (2015) Advanced pitch angle control based on fuzzy logic for variable-speed wind turbine systems. IEEE Transac Energy Conv 30(2):578–587

    Article  Google Scholar 

  23. Asgharnia A, Shahnazi R, Jamali A (2018) Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms. ISA Transac 79:27–44

    Article  Google Scholar 

  24. Yin XX, Lin YG, Li W, Liu HW, Gu YJ (2015) Adaptive sliding mode back-stepping pitch angle control of a variable-displacement pump controlled pitch system for wind turbines. ISA Transac 58(5):629–634

    Article  Google Scholar 

  25. Lasheen A, Elnaggar M, Yassin H (2019) Adaptive control design and implementation for collective pitch in wind energy conversion systems. ISA Transac 102:251–263

    Article  Google Scholar 

  26. Yuan Y, Tang J (2017) Adaptive pitch control of wind turbine for load mitigation under structural uncertainties. Renew Energy 105:483–494

    Article  Google Scholar 

  27. Wang Y, Jiang B, Wu Z, Xie S, Peng Y (2020) Adaptive sliding mode fault-tolerant fuzzy tracking control with application to unmanned marine vehicles. IEEE Transac Syst, Man, Cybern: Syst. https://doi.org/10.1109/TSMC.2020.2964808

    Article  Google Scholar 

  28. Wang Y, Xie X, Chadli M, Xie S, Peng Y (2020) Sliding mode control of fuzzy singularly perturbed descriptor systems. IEEE Transac Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.2998519

    Article  Google Scholar 

  29. Liu L, Liu YJ, Chen A, Tong S, Chen CP (2020) Integral Barrier Lyapunov function-based adaptive control for switched nonlinear systems. Sci China Info Sci 63(3):212–225

    MathSciNet  Google Scholar 

  30. Liu L, Liu Y-J, Li D, Tong S, Wang Z (2020) Barrier Lyapunov function-based adaptive fuzzy ftc for switched systems and its applications to resistance-inductance-capacitance circuit system. IEEE Transac Cybern 50(8):3491–3502

    Article  Google Scholar 

  31. Tang L, Ma D, Zhao J (2019) Adaptive neural control for switched non-linear systems with multiple tracking error constraints. IET Signal Process 13(3):330–337

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No.52005232,51775543), National Natural Science Foundation of Jiangsu Province, China (Grant No.BK20201024), the Natural Science Foundation of Jiangsu Normal University, China (Grant No. 19XSRX016) and the Key Research and Development Project of Xuzhou (Grant No. KC17014).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lianchao Sheng.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

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

Sheng, L., Li, M., Li, Y. et al. Auto Disturbance Rejection Control Strategy of Wind Turbine Permanent Magnet Direct Drive Individual Variable Pitch System Under Load Excitation. J. Electr. Eng. Technol. 16, 1607–1617 (2021). https://doi.org/10.1007/s42835-021-00672-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-021-00672-1

Keywords

Navigation