Doubly fed induction wind generators model and field orientation vector control design and implementation on FPGA

  • Houssam NahiliaEmail author
  • Mohamed Boudour
  • Alben Cardenas
  • Kodjo Agbossou
  • Mamadou Lamine Doumbia


Dynamic systems emulators are very efficient techniques to design a system that mimics a precise behaviour of real systems. Moreover, they are designed to reduce costs as well as to give more flexibility concerning experimental purpose. The objective of this work is to develop an FPGA-based system, that emulates the overall doubly fed induction generator (DFIG) and active and reactive power vector control scheme for wind generators applications. The advantage of such an approach is to design a system which is more open to structural changes, has the possibility to be changed and could be adapted to several operating scenarios at lower costs. The DFIG with vector control scheme have been modelled using Xilinx System Generator and implemented in FPGA. The efficiency of this approach is assessed, yet, proofed considering reference tracking attitude of the controlled machine comparing to software simulation technique.


FPGA Xilinx–Simulink co-simulation DFIG Wind generators modelling Vector control 


  1. 1.
    Khare V, Nema S, Baredar P (2016) Solar-wind hybrid renewable energy system: a review. Renew Sustain Energy Rev 58:23–33CrossRefGoogle Scholar
  2. 2.
    Huda ASN, Živanović R (2017) Large-scale integration of distributed generation into distribution networks: study objectives, review of models and computational tools. Renew Sustain Energy Rev 76:974–988CrossRefGoogle Scholar
  3. 3.
    Tanwar SS, Khatod DK (2017) Techno-economic and environmental approach for optimal placement and sizing of renewable dgs in distribution system. Energy 127:52–67CrossRefGoogle Scholar
  4. 4.
    Nahilia H, Boudour M (2015) Voltage stability analysis and optimal distributed generators placement study on algerian power network. In: 2015 3rd international conference on control, engineering & information technology (CEIT). IEEE, pp 1–5Google Scholar
  5. 5.
    Shahbazi M, Poure P, Saadate S, Zolghadri MR (2011) Five-leg converter topology for wind energy conversion system with doubly fed induction generator. Renew Energy 36(11):3187–3194CrossRefGoogle Scholar
  6. 6.
    MD Faruque O, Strasser T, Lauss G, Jalili-Marandi V, Forsyth P, Dufour C, Dinavahi V, Monti A, Kotsampopoulos P, Martinez JA et al (2015) Real-time simulation technologies for power systems design, testing, and analysis. IEEE Power Energy Technol Syst J 2(2):63–73CrossRefGoogle Scholar
  7. 7.
    Mahmoudi H, Ed-Dahmani C, El Azzaoui M (2019) An FPGA-based control of the PMSG on variable wind speed turbine. In: Derbel N, Zhu Q (eds) Modeling, identification and control methods in renewable energy systems. Springer, Berlin, pp 357–372CrossRefGoogle Scholar
  8. 8.
    Schäffer L, Kincses Z (2015) Implementation of an FPGA-based wind turbine HIL modelGoogle Scholar
  9. 9.
    Gaillard A, Poure P, Saadate S (2013) FPGA-based reconfigurable control for switch fault tolerant operation of WECS with DFIG without redundancy. Renew energy 55:35–48CrossRefGoogle Scholar
  10. 10.
    Essoussi IM, Bouallegue A, Khedher A (2015) 3 kw wind turbine emulator implementation on FPGA using matlab/simulink. Int J Renew Energy Res (IJRER) 5(4):1154–1163Google Scholar
  11. 11.
    Chen H, Sun S, Aliprantis DC, Zambreno J (2009) Dynamic simulation of electric machines on FPGA boards. In: 2009 IEEE international electric machines and drives conference, IEMDC’09. IEEE, pp 1523–1528Google Scholar
  12. 12.
    Chen H, Sun S, Aliprantis DC, Zambreno J (2010) Dynamic simulation of DFIG wind turbines on FPGA boards. In: Power and energy conference at Illinois (PECI), 2010. IEEE, pp 39–44Google Scholar
  13. 13.
    El Azzaoui M, Mahmoudi H, Ed-dahmani C (2016) Backstepping control of the doubly fed induction generator using xilinx system generator for implementation on FPGA. In: 2016 5th international conference on multimedia computing and systems (ICMCS). IEEE, pp 599–604Google Scholar
  14. 14.
    El Azzaoui M, Mahmoudi H, Boudaraia K, Ed-dahmani C (2017) FPGA implementation of super twisting sliding mode control of the doubly fed induction generator. In: 2017 14th international multi-conference on systems, signals & devices (SSD). IEEE, pp 649–654Google Scholar
  15. 15.
    Karimi S, Gaillard A, Poure P, Saadate S (2008) FPGA-based real-time power converter failure diagnosis for wind energy conversion systems. IEEE Trans Ind Electron 55(12):4299–4308CrossRefGoogle Scholar
  16. 16.
    Cardenas A, Guzman C, Agbossou K (2012) Development of a FPGA based real-time power analysis and control for distributed generation interface. IEEE Trans Power Syst 27(3):1343–1353CrossRefGoogle Scholar
  17. 17.
    Cardenas A, Agbossou K (2012) Experimental evaluation of voltage positive feedback based anti-islanding algorithm: multi-inverter case. IEEE Trans Energy Convers 27(2):498–506CrossRefGoogle Scholar
  18. 18.
    Guzman C, Cardenas A, Agbossou K (2014) Load sharing strategy for autonomous AC microgrids based on FPGA implementation of ADALINE&FLL. IEEE Trans Energy Convers 29(3):663–672CrossRefGoogle Scholar
  19. 19.
    Guzmán C, Agbossou K, Cardenas A (2018) Real-time emulation of residential buildings by hardware solution of multi-layer model. IEEE Trans Smart Grid.
  20. 20.
    Hamane B, Benghanemm M, Bouzid AM, Belabbes A, Bouhamida M, Draou A (2012) Control for variable speed wind turbine driving a doubly fed induction generator using fuzzy-pi control. Energy Procedia 18:476–485CrossRefGoogle Scholar
  21. 21.
    Babouri R, Aouzellag D, Ghedamsi K (2013) Integration of doubly fed induction generator entirely interfaced with network in a wind energy conversion system. Energy Procedia 36:169–178CrossRefGoogle Scholar
  22. 22.
    Weng Y-T, Hsu Y-Y (2016) Reactive power control strategy for a wind farm with DFIG. Renew Energy 94:383–390CrossRefGoogle Scholar
  23. 23.
    Charles CMR, Vinod V, Jacob A (2016) Field oriented control of DFIG based wind energy system using battery energy storage system. Procedia Technol 24:1203–1210CrossRefGoogle Scholar
  24. 24.
    Taher SA, Arani ZD, Rahimi M, Shahidehpour M (2017) Model predictive fuzzy control for enhancing FRT capability of DFIG-based WT in real-time simulation environment. Energy Syst 9:1–21Google Scholar
  25. 25.
    Nahilia H, Boudour M, Gonzalez AC, Doumbia ML, Agbossou K (2017) Doubly fed induction generator based wind generator dynamic simulation considering power extraction maximization. In: 2017 6th International conference on systems and control (ICSC). IEEE, pp 595–600Google Scholar
  26. 26.
    Koumba PM, Cheriti A, Doumbia ML, El Moubarek BA, Chaoui H (2017) Wind turbine control based on a permanent magnet synchronous generator connected to an isolated electrical network. In: 2017 IEEE electrical power and energy conference (EPEC). IEEE, pp 1–7Google Scholar
  27. 27.
    Mulolani F (2017) Performance of direct power controlled grid-connected voltage source convertersGoogle Scholar
  28. 28.
    Belmokhtar K, Doumbia ML, Agbossou K (2014) Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator). Energy 76:679–693CrossRefGoogle Scholar
  29. 29.
    Kuzhali M, Isac SJ, Poongothai S (2019) Improvement of low voltage ride through capability of grid-connected DFIG WTS using fuzzy logic controller. In: International conference on intelligent computing and applications. Springer, pp 349–359Google Scholar
  30. 30.
    Bai Y, Roth ZS (2019) Classical linear control systems–PID control systems. In: Classical and modern controls with microcontrollers. Springer, pp 195–321Google Scholar
  31. 31.
    Fathoni MF, Wuryandari AI (2015) Comparison between Euler, Heun, Runge–Kutta and Adams–Bashforth–Moulton integration methods in the particle dynamic simulation. In: 2015 4th international conference on interactive digital media (ICIDM). IEEE, pp 1–7Google Scholar
  32. 32.
    Raja MAZ, Umar M, Sabir Z, Khan JA, Baleanu D (2018) A new stochastic computing paradigm for the dynamics of nonlinear singular heat conduction model of the human head. Eur Phys J Plus 133(9):364CrossRefGoogle Scholar
  33. 33.
    Sabir Z, Manzar MA, Raja MAZ, Sheraz M, Wazwaz AM (2018) Neuro-heuristics for nonlinear singular thomas-fermi systems. Appl Soft Comput 65:152–169CrossRefGoogle Scholar
  34. 34.
    Yepes AG, Freijedo FD, Doval-Gandoy J, Lopez O, Malvar J, Fernandez-Comesana P (2010) Effects of discretization methods on the performance of resonant controllers. IEEE Trans Power Electron 25(7):1692–1712CrossRefGoogle Scholar
  35. 35.
    Leon AE, Mauricio JM, Solsona JA (2012) Fault ride-through enhancement of DFIG-based wind generation considering unbalanced and distorted conditions. IEEE Trans Energy Convers 27(3):775–783CrossRefGoogle Scholar
  36. 36.
    Hossain ME (2017) Application of gaussian mixture regression model for short-term wind speed forecasting. In: 2017 North American power symposium (NAPS). IEEE, pp 1–6Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.LSEI LaboratoryUniversity of Science and Technology Houari BoumedieneAlgiersAlgeria
  2. 2.Hydrogen Research InstituteUniversité du Québec à Trois-RivièresTrois-RivièresCanada

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