LVRT Capability Improvement in a Grid-Connected DFIG Wind Turbine System Using Neural Network-Based Dynamic Voltage Restorer

  • Arun Kumar Puliyadi Kubendran
  • L. Ashok Kumar
Conference paper


The wind power plant is one of the fastest growing electrical power sources. The integration of wind turbine into the power grid originates various power quality problems. Low-voltage ride through (LVRT) capability improvement is impregnating exigency in recent power quality issue, which is acquainted with various renewable power generation resources like solar PV and wind power plant. Dynamic voltage restorer (DVR) is a custom power device (CPD), which is connected in series with the electrical system, and it is a contemporary and efficient CPD expended in the distribution system to curb the power quality problems. In this paper, LVRT capability of doubly fed induction generator (DFIG) wind turbine system connected to a power grid is enhanced by means of DVR. LVRT capability is improved by the introduction of neural network controller. This controller fulfils the various grid code requirements. Thus, the performance of the DVR becomes far superior by the robust control technique. The simulation results were compared with conventional PI controller and the proposed artificial neural network (ANN) controller. It is proved that the proposed system raises the reliability of grid-connected DFIG wind turbine system.


Wind farm Low-voltage ride through DVR Custom power device DFIG Neural network Power quality 



artificial neural network


custom power device


doubly fed induction generator


dynamic voltage restorer


electromotive force


feed-forward neural network


grid side converter


low-voltage ride through


power quality


rotor side converter


static synchronous compensator


static VAR compensator


wind energy conversion system


wind turbine


wind turbine generator


  1. 1.
    Chai C, Lee WJ, P F et al (2005) System impact study for the interconnection of wind generation and utility system. IEEE Trans Ind Appl 41(1):163–168CrossRefGoogle Scholar
  2. 2.
    Kubendran AKP, Loganathan AK (2017) Detection and classification of complex power quality disturbances using S-transform amplitude matrix-based decision tree for different noise levels. Int Trans Electr Energy Syst 27(4):1–12. Scholar
  3. 3.
    Kumar PKA, Vijayalakshmi VJ, Karpagam J, Hemapriya CK (2016) Classification of power quality events using support vector machine and S-Transform. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, pp 279–284.
  4. 4.
    Case C (2006) Connecting wind farms to the grid-what you need to know. VancouverGoogle Scholar
  5. 5.
    Xing Z, Longyun Z, Shuying Y et al (2008) Low voltage ride-through technologies in wind turbine generation. Proc CSU-EPSA 20(2):1–8Google Scholar
  6. 6.
    Ottersten R, Petersson A, Pietilainen K (2004) Voltage sag response of PWM rectifiers for variable-speed wind turbines. Nordic workshop on power and industrial electronics, Chalmers University of TechnologyGoogle Scholar
  7. 7.
    Chompoo-inwai C, Yingvivatanapong C, Methaprayoon K et al (2005) Reactive compensation techniques to improve the ride-through capability of wind turbine during disturbance. IEEE Trans Ind Appl 41(3):666–672CrossRefGoogle Scholar
  8. 8.
    Muller S, Deicke M, De Doncker RW (2002) Doubly fed induction generator systems for wind turbine. IEEE Ind Appl Mag 3:26–33CrossRefGoogle Scholar
  9. 9.
    Pena R, Clare JC, Asher GM (1996) Doubly fed induction generator using back-to-back PWM converts and its application to variable speed wind-energy generation. IEE Proc Electr Power Appl 143:231–241CrossRefGoogle Scholar
  10. 10.
    Arunkumar PK, Kannan SM, Selvalakshmi I (2016) Low voltage ride through capability improvement in a grid connected wind energy conversion system using STATCOM. International Conference on Energy Efficient Technologies for Sustainability (ICEETS), Nagercoil, pp 603–608Google Scholar
  11. 11.
    Solar PV and wind energy conversion systems: an introduction to theory, modeling with MATLAB/SIMULINK, and the role of soft computing techniques, S Sumathi, LA Kumar, P Surekha, Springer, Green Energy and Technology,, ISBN: 9783319149400, 9783319149417 (online)CrossRefGoogle Scholar
  12. 12.
    Tapia A, Tapia G, Ostolaza JX, Saenz JR (2003) Modeling and control of a wind turbine driven doubly fed induction generator. IEEE Trans Energy Conver 18:194–204CrossRefGoogle Scholar
  13. 13.
    Lei Y, Mullane A, Lightbody G, Yacamini R (2006) Modeling of the wind turbine with a doubly fed induction generator for grid integration studies. IEEE Trans Energy Conver 21(1):257–264CrossRefGoogle Scholar
  14. 14.
    Akhmatoy V, Krudsen H (1999) Modelling of windmill induction generator in dynamic simulation programs. In: Proceedings of IEEE International conference on power technology, Budapest, Hungary, paper no. 108. AugGoogle Scholar
  15. 15.
    Li H, Chen Z (Jun. 2008) Overview of generator topologies for wind turbines. IET Proc Renew Power Gener 2(2):123–138CrossRefGoogle Scholar
  16. 16.
    Mihet-Popa L, Blaabrierg F (2004) Wind turbine generator modeling and simulation where rotational speed is the controlled variable. IEEE Trans Ind Appl 40(1):3–10CrossRefGoogle Scholar
  17. 17.
    Chowary BH, Chellapilla S (2006) Doubly-fed induction generator for variable speed wind power generation. IEEE Trans Electric Power Sys Res 76:786–800CrossRefGoogle Scholar
  18. 18.
    Roldán-Pérez J, García-Cerrada A, Ochoa-Giménez M, Zamora-Macho JL (Oct. 2016) On the power flow limits and control in series-connected custom power devices. IEEE Trans Power Electron 31(10):7328–7338Google Scholar
  19. 19.
    Eklashossain, Padmanaban S (2018) Analysis and mitigation of power quality issues in distributed generation systems using custom power devices. IEEE Access 6:16816–16833CrossRefGoogle Scholar
  20. 20.
    Kumar PKA, Vivekanandan S, Kumar CK, and Chinnaiyan VK (2016) Neural network tuned fuzzy logic power system stabilizer design for SMIB. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, pp 446–451.
  21. 21.
    Kumar PKA, Uthirasamy R, Saravanan G, Ibrahim AM (2016) AGC performance enhancement using ANN. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, pp. 452–456.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Arun Kumar Puliyadi Kubendran
    • 1
  • L. Ashok Kumar
    • 2
  1. 1.Saranathan College of EngineeringTiruchirappalliIndia
  2. 2.Department of Electrical and Electronics EngineeringPSG College of TechnologyCoimbatoreIndia

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