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Fuzzy logic control based maximum power tracking of a wind energy system

Author

Listed:
  • Mohamed, Amal Z.
  • Eskander, Mona N.
  • Ghali, Fadia A.

Abstract

In this paper a utility interactive wind energy conversion system (WECS) with an asynchronous (AC–DC–AC) link is described. The control system has the objective of identifying and extracting the maximum power from the wind energy system and transferring this power to utility. A fuzzy logic control (FLC) technique has been implemented to design the tracking controller of the WECS. A wind speed step model was used in the design phase. The performance of the WECS with the proposed fuzzy logic controller is tested using real meteorological data. Its robustness and effectiveness are demonstrated by the simulation results of the controlled system.

Suggested Citation

  • Mohamed, Amal Z. & Eskander, Mona N. & Ghali, Fadia A., 2001. "Fuzzy logic control based maximum power tracking of a wind energy system," Renewable Energy, Elsevier, vol. 23(2), pages 235-245.
    • Handle: RePEc:eee:renene:v:23:y:2001:i:2:p:235-245
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    Cited by:

    1. Ram, J.Prasanth & Rajasekar, N. & Miyatake, Masafumi, 2017. "Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1138-1159.
    2. Alizadeh, Mojtaba & Kojori, Shokrollah Shokri, 2015. "Augmenting effectiveness of control loops of a PMSG (permanent magnet synchronous generator) based wind energy conversion system by a virtually adaptive PI (proportional integral) controller," Energy, Elsevier, vol. 91(C), pages 610-629.
    3. Mokhtari, Yacine & Rekioua, Djamila, 2018. "High performance of Maximum Power Point Tracking Using Ant Colony algorithm in wind turbine," Renewable Energy, Elsevier, vol. 126(C), pages 1055-1063.
    4. Zaragoza, Jordi & Pou, Josep & Arias, Antoni & Spiteri, Cyril & Robles, Eider & Ceballos, Salvador, 2011. "Study and experimental verification of control tuning strategies in a variable speed wind energy conversion system," Renewable Energy, Elsevier, vol. 36(5), pages 1421-1430.
    5. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    6. Jurado, Francisco & Saenz, José R, 2002. "Neuro-fuzzy control for autonomous wind–diesel systems using biomass," Renewable Energy, Elsevier, vol. 27(1), pages 39-56.
    7. Battisti, L. & Benini, E. & Brighenti, A. & Dell’Anna, S. & Raciti Castelli, M., 2018. "Small wind turbine effectiveness in the urban environment," Renewable Energy, Elsevier, vol. 129(PA), pages 102-113.
    8. Delarue, Ph. & Bouscayrol, A. & Tounzi, A. & Guillaud, X. & Lancigu, G., 2003. "Modelling, control and simulation of an overall wind energy conversion system," Renewable Energy, Elsevier, vol. 28(8), pages 1169-1185.
    9. Billy Muhando, Endusa & Senjyu, Tomonobu & Urasaki, Naomitsu & Yona, Atsushi & Kinjo, Hiroshi & Funabashi, Toshihisa, 2007. "Gain scheduling control of variable speed WTG under widely varying turbulence loading," Renewable Energy, Elsevier, vol. 32(14), pages 2407-2423.
    10. Tripathi, S.M. & Tiwari, A.N. & Singh, Deependra, 2015. "Grid-integrated permanent magnet synchronous generator based wind energy conversion systems: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1288-1305.
    11. Assareh, Ehsanolah & Biglari, Mojtaba, 2015. "A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1023-1037.
    12. Amirsoheil Honarbari & Sajad Najafi-Shad & Mohsen Saffari Pour & Seyed Soheil Mousavi Ajarostaghi & Ali Hassannia, 2021. "MPPT Improvement for PMSG-Based Wind Turbines Using Extended Kalman Filter and Fuzzy Control System," Energies, MDPI, vol. 14(22), pages 1-16, November.
    13. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    14. Senjyu, Tomonobu & Ochi, Yasutaka & Kikunaga, Yasuaki & Tokudome, Motoki & Yona, Atsushi & Muhando, Endusa Billy & Urasaki, Naomitsu & Funabashi, Toshihisa, 2009. "Sensor-less maximum power point tracking control for wind generation system with squirrel cage induction generator," Renewable Energy, Elsevier, vol. 34(4), pages 994-999.
    15. Ganjefar, Soheil & Ghassemi, Ali Akbar & Ahmadi, Mohamad Mehdi, 2014. "Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network," Energy, Elsevier, vol. 67(C), pages 444-453.
    16. Anto Anbarasu Yesudhas & Young Hoon Joo & Seong Ryong Lee, 2022. "Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed," Energies, MDPI, vol. 15(9), pages 1-17, April.

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