International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 6, Issue 9 (September 2019), Pages: 107-116

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 Original Research Paper

 Title: DFIG control: A fuzzy approach

 Author(s): Alnufaie Lafi *

 Affiliation(s):

 College of Engineering, Shaqra University, Shaqraa, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-2453-2637

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.09.015

 Abstract:

In this paper, we are going to discuss the fuzzy logic techniques and their importance in the control of the nonlinear systems.  The double fed Induction generator (DFIG) is wildly used in wind energy conversion systems. The control of DFIG is very complicated due to its strong nonlinearities. The first case is a controller with 3 sets in inputs and outputs. The second case is a controller with 5 sets in inputs and outputs. The third case is a controller with 7 sets in inputs and outputs. The objective of this paper is to propose a new control strategy based on fuzzy logic in order to control the power of the wind turbine and make it adaptable to different constraints. A simulation study is done to validate this control strategy. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: DFIG, Wind energy conversion system, Fuzzy control

 Article History: Received 18 March 2019, Received in revised form 10 July 2019, Accepted 16 July 2019

 Acknowledgement:

No Acknowledgement.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Lafi A (2019). DFIG control: A fuzzy approach. International Journal of Advanced and Applied Sciences, 6(9): 107-116

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 

 Tables

 Table 1 Table 2 Table 3 

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 References (14) 

  1. Agarwal V, Aggarwal RK, Patidar P, and Patki C (2009). A novel scheme for rapid tracking of maximum power point in wind energy generation systems. IEEE Transactions on Energy Conversion, 25(1): 228-236. https://doi.org/10.1109/TEC.2009.2032613   [Google Scholar]
  2. Alaboudy AHK, Daoud AA, Desouky SS, and Salem AA (2013). Converter controls and flicker study of PMSG-based grid connected wind turbines. Ain Shams Engineering Journal, 4(1): 75-91. https://doi.org/10.1016/j.asej.2012.06.002   [Google Scholar]
  3. Amine BM, Souhila Z, and Tayeb A (2014). Adaptive fuzzy logic control of wind turbine emulator. International Journal of Power Electronics and Drive Systems, 4(2): 241-255. https://doi.org/10.11591/ijpeds.v4i2.5809   [Google Scholar]
  4. Bouscayrol A, Delarue P, and Guillaud X (2005). Power strategies for maximum control structure of a wind energy conversion system with a synchronous machine. Renewable Energy, 30(15): 2273-2288. https://doi.org/10.1016/j.renene.2005.03.005   [Google Scholar]
  5. Ebeed M, NourEldeen O, and Ebrahim AA (2013). Assessing behavior of the Outer Crowbar Protection with the DFIG during grid fault. In the 2nd International Conference on Energy Systems and Technologies, Cairo, Egypt: 18-21.   [Google Scholar]
  6. Hussain A, Arif SM, and Aslam M (2017). Emerging renewable and sustainable energy technologies: State of the art. Renewable and Sustainable Energy Reviews, 71: 12-28. https://doi.org/10.1016/j.rser.2016.12.033   [Google Scholar]
  7. Ismail MM and Bendary AF (2016). Protection of DFIG wind turbine using fuzzy logic control. Alexandria Engineering Journal, 55(2): 941-949. https://doi.org/10.1016/j.aej.2016.02.022   [Google Scholar]
  8. Kesraoui M, Korichi N, and Belkadi A (2011). Maximum power point tracker of wind energy conversion system. Renewable Energy, 36(10): 2655-2662. https://doi.org/10.1016/j.renene.2010.04.028   [Google Scholar]
  9. Mi C, Filippa M, Shen J, and Natarajan N (2004). Modeling and control of a variable-speed constant-frequency synchronous generator with brushless exciter. IEEE Transactions on Industry Applications, 40(2): 565-573. https://doi.org/10.1109/TIA.2004.824504   [Google Scholar]
  10. Tapia A, Tapia G, Ostolaza JX, and Saenz JR (2003). Modeling and control of a wind turbine driven doubly fed induction generator. IEEE Transactions on Energy Conversion, 18(2): 194-204. https://doi.org/10.1109/TEC.2003.811727   [Google Scholar]
  11. Thongam JS, Bouchard P, Beguenane R, Okou AF, and Merabet A (2011). Control of variable speed wind energy conversion system using a wind speed sensor less optimum speed MPPT control method. In the IECON 2011-37th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Melbourne, Australia: 855-860. https://doi.org/10.1109/IECON.2011.6119422   [Google Scholar]
  12. Vlad C, Munteanu I, Bratcu AI, and Ceangă E (2010). Output power maximization of low-power wind energy conversion systems revisited: Possible control solutions. Energy Conversion and Management, 51(2): 305-310. https://doi.org/10.1016/j.enconman.2009.09.026   [Google Scholar]
  13. Wang Y, Phung BT, and Ravishankar J (2015). Fault analysis of an islanded micro-grid with doubly fed induction generator based wind turbine. In the International Conference on Electricity Distribution, CIRED, Lyon, France: 1-5.   [Google Scholar]
  14. Yang L, Xu Z, Ostergaard J, Dong ZY, and Wong KP (2012). Advanced control strategy of DFIG wind turbines for power system fault ride through. IEEE Transactions on Power Systems, 27(2): 713-722. https://doi.org/10.1109/TPWRS.2011.2174387   [Google Scholar]