Advertisement

Hybrid Fuzzy Logic-Based MPPT for Wind Energy Conversion System

  • Vankayalapati Govinda ChowdaryEmail author
  • V. Udhay Sankar
  • Derick Mathew
  • CH Hussaian Basha
  • C. Rani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)

Abstract

Maximum power can be extricated when the turbine keeps running at a consistent and constant speed by using all the vitality present in the wind. The turbine can keep running at a steady speed just when the breeze speed is consistent. The wind vitality being wild in nature, maximum power must be achieved by making the turbine to keep running at the specific breeze speed. To achieve most extreme power, distinctive sorts of maximum power point tracking (MPPT) procedures are utilized. So as to comprehend prudent and proficient power age utilizing wind turbines, modification of fuzzy-based MPPT method is displayed and results are compared with different MPPT techniques for wind energy conversion system have been done and are introduced in subtleties.

Keywords

Fuzzy logic Incremental conductance Perturb and observe Wind energy conversion system 

Nomenclature

\(\rho\)

Air density (1.2 kg/m3)

Cp

Power coefficient

\(\beta\)

Incident angle of the blade

Vin

Input voltage

Vo

Output voltage

Pw

Wind power

ηg

Generator efficiency

ηm

Motor efficiency

I

Current

Pe

Electric power generated

E

Error

DP

Deviation of power over a small time interval

DV

Deviation of voltage over a small time interval

DI

Deviation of current over a small time interval

CE

Deviation in error

References

  1. 1.
    Global Trends in Sustainable Energy Investment 2007: Analysis of Trends and Issues in the Financing of Renewable Energy and Energy Efficiency in OECD and Developing Countries (PDF), p. 3. United Nations Environment Programme (2007). unep.org. Archived (PDF) from the original on 13 Oct 2014. Accessed 13 Oct 2014Google Scholar
  2. 2.
    Leone, S.: U.N. Secretary-General: Renewables Can End Energy Poverty. Renewable Energy World (2011)Google Scholar
  3. 3.
    GWEC: Global Wind Report Annual Market Update. Gwec.net. Accessed 20 May 2017Google Scholar
  4. 4.
    Installed Capacity of Wind Power Projects in India. Accessed 7 Apr 2018Google Scholar
  5. 5.
    Global Wind Statistics 2017 (PDF)Google Scholar
  6. 6.
  7. 7.
    Tounsi, A., Abid, H., Kharrat, M., Elleuch, K.: MPPT algorithm for wind energy conversion system based on PMSG. In: 2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Monastir, Tunisia, pp. 533–538 (2017)Google Scholar
  8. 8.
    Wafa, H., Aicha, A., Mouna, B.H.: Steps of duty cycle effects in P&O MPPT algorithm for PV system. In: 2017 International Conference on Green Energy Conversion Systems (GECS), Hammamet, pp. 1–4 (2017)Google Scholar
  9. 9.
    Masood, B., Siddique, M.S., Asif, R.M., Zia-ul-Haq, M.: Maximum power point tracking using hybrid perturb & observe and incremental conductance techniques. In: 2014 4th International Conference on Engineering Technology and Technopreneurship (ICE2T), Kuala Lumpur, pp. 354–359 (2014)Google Scholar
  10. 10.
    Khadidja, S., Mountassar, M., M’hamed, B.: Comparative study of incremental conductance and perturb & observe MPPT methods for photovoltaic system. In: 2017 International Conference on Green Energy Conversion Systems (GECS), Hammamet, pp. 1–6 (2017)Google Scholar
  11. 11.
    Lahfaoui, B., Zouggar, S., Elhafyani, M.L., Seddik, M.: Experimental study of P&O MPPT control for wind PMSG turbine. In: 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC), Marrakech, pp. 1–6 (2015)Google Scholar
  12. 12.
    Lee, J.H., Bae, H., Cho, B.H.: Advanced incremental conductance MPPT algorithm with a variable step size. In: 2006 12th International Power Electronics and Motion Control Conference, Portoroz, pp. 603–607 (2006)Google Scholar
  13. 13.
    Heydari, M., Smedley, K.: Comparison of maximum power point tracking methods for medium to high power wind energy systems. In: 2015 20th Conference on Electrical Power Distribution Networks Conference (EPDC), Zahedan, pp. 184–189 (2015)Google Scholar
  14. 14.
    Abdullah, M.A., Yatim, A.H.M., Tan, C.W.: A study of maximum power point tracking algorithms for wind energy system. In: 2011 IEEE Conference on Clean Energy and Technology (CET), Kuala Lumpur, pp. 321–326 (2011)Google Scholar
  15. 15.
    Kumar, D., Chatterjee, K.: A review of conventional and advanced MPPT algorithms for wind energy systems. Renew. Sustain. Energy Rev. 55, 957–970 (2016). ISSN 1364-0321CrossRefGoogle Scholar
  16. 16.
    Mehta, G., Dwivedi, M., Yadav, V.K.: Comparison of advance intelligence algorithms for maximum power point tracking. In: 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), Mathura, pp. 262–267 (2017)Google Scholar
  17. 17.
    Heshmatian, S., Kazemi, A., Khosravi, M., Khaburi, D.A.: Fuzzy logic based MPPT for a wind energy conversion system using sliding mode control. In: 2017 8th Power Electronics, Drive Systems & Technologies Conference (PEDSTC), Mashhad, pp. 335–340 (2017)Google Scholar
  18. 18.
    Sarvi, M., et al.: A New Method for Rapid Maximum Power Point Tracking of PMSG Wind Generator Using PSO_Fuzzy Logic (2013)Google Scholar
  19. 19.
    Rajvikram, M., Renuga, P., Swathisriranjani, M.: Fuzzy based MPPT controller’s role in extraction of maximum power in wind energy conversion system. In: 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, pp. 713–719 (2016)Google Scholar
  20. 20.
    Dida, A., Benattous, D.: Fuzzy logic based sensorless MPPT algorithm for wind turbine system driven DFIG. In: 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), Tlemcen, pp. 1–6 (2015)Google Scholar
  21. 21.
    Soufi, Y., Bechouat, M., Kahla, S., Bouallegue, K.: Maximum power point tracking using fuzzy logic control for photovoltaic system. In: 2014 International Conference on Renewable Energy Research and Application (ICRERA), Milwaukee, WI, pp. 902–906 (2014)Google Scholar
  22. 22.
    Marmouh, S., Boutoubat, M., Mokrani, L.: MPPT fuzzy logic controller of a wind energy conversion system based on a PMSG. In: 2016 8th International Conference on Modelling, Identification and Control (ICMIC), Algiers, pp. 296–302 (2016)Google Scholar
  23. 23.
    Sl-Subhi, A., Alsumiri, M., Alalwani, S.: Novel MPPT algorithm for low cost wind energy conversion systems. In: 2017 International Conference on Advanced Control Circuits Systems (ACCS) Systems & 2017 International Conference on New Paradigms in Electronics & Information Technology (PEIT), Alexandria, Egypt, pp. 144–148 (2017)Google Scholar
  24. 24.
    Harrabi, N., Souissi, M., Aitouche, A., Chaabane, M.: MPPT algorithm for wind energy generation system using T-S fuzzy modeling. In: 2016 5th International Conference on Systems and Control (ICSC), Marrakesh, pp. 157–162 (2016)Google Scholar
  25. 25.
    Patil, S.N., Prasad, R.C.: Design and development of MPPT algorithm for high efficient DC–DC converter for wind energy system connected to grid. In: 2015 International Conference on Computer, Communication and Control (IC4), Indore, pp. 1–7 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Vankayalapati Govinda Chowdary
    • 1
    Email author
  • V. Udhay Sankar
    • 1
  • Derick Mathew
    • 1
  • CH Hussaian Basha
    • 1
  • C. Rani
    • 1
  1. 1.School of Electrical EngineeringVIT UniversityVelloreIndia

Personalised recommendations