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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

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  • Ganjefar, Soheil
  • Ghassemi, Ali Akbar
  • Ahmadi, Mohamad Mehdi

Abstract

In this paper, a quantum neural network (QNN) is used as controller in the adaptive control structures to improve efficiency of the maximum power point tracking (MPPT) methods in the wind turbine system. For this purpose, direct and indirect adaptive control structures equipped with QNN are used in tip-speed ratio (TSR) and optimum torque (OT) MPPT methods. The proposed control schemes are evaluated through a battery-charging windmill system equipped with PMSG (permanent magnet synchronous generator) at a random wind speed to demonstrate transcendence of their effectiveness as compared to PID controller and conventional neural network controller (CNNC).

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  • 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.
    • Handle: RePEc:eee:energy:v:67:y:2014:i:c:p:444-453
    DOI: 10.1016/j.energy.2014.02.023
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    Cited by:

    1. Lucky Dube & Graham C. Garner & Karen S. Garner & Maarten J. Kamper, 2023. "Simple and Robust MPPT Current Control of a Wound Rotor Synchronous Wind Generator," Energies, MDPI, vol. 16(7), pages 1-21, April.
    2. Ganjefar, Soheil & Mohammadi, Ali, 2016. "Variable speed wind turbines with maximum power extraction using singular perturbation theory," Energy, Elsevier, vol. 106(C), pages 510-519.
    3. Mseddi, Amina & Le Ballois, Sandrine & Aloui, Helmi & Vido, Lionel, 2019. "Robust control of a wind conversion system based on a hybrid excitation synchronous generator: A comparison between H∞ and CRONE controllers," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 453-476.
    4. Ganjefar, Soheil & Ghasemi, Ali Akbar, 2014. "A novel-strategy controller design for maximum power extraction in stand-alone windmill systems," Energy, Elsevier, vol. 76(C), pages 326-335.
    5. Dinh-Chung Phan & Shigeru Yamamoto, 2015. "Maximum Energy Output of a DFIG Wind Turbine Using an Improved MPPT-Curve Method," Energies, MDPI, vol. 8(10), pages 1-19, October.
    6. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    7. Xuan Chau Le & Minh Quan Duong & Kim Hung Le, 2022. "Review of the Modern Maximum Power Tracking Algorithms for Permanent Magnet Synchronous Generator of Wind Power Conversion Systems," Energies, MDPI, vol. 16(1), pages 1-25, December.
    8. Suchithra, R. & Ezhilsabareesh, K. & Samad, Abdus, 2019. "Optimization based higher order sliding mode controller for efficiency improvement of a wave energy converter," Energy, Elsevier, vol. 187(C).
    9. 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.
    10. Muthana Alrifai & Mohamed Zribi & Mohamed Rayan, 2016. "Feedback Linearization Controller for a Wind Energy Power System," Energies, MDPI, vol. 9(10), pages 1-23, September.
    11. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    12. Yashar Mousavi & Geraint Bevan & Ibrahim Beklan Küçükdemiral & Afef Fekih, 2021. "Maximum Power Extraction from Wind Turbines Using a Fault-Tolerant Fractional-Order Nonsingular Terminal Sliding Mode Controller," Energies, MDPI, vol. 14(18), pages 1-16, September.
    13. Phan, Dinh-Chung & Yamamoto, Shigeru, 2016. "Rotor speed control of doubly fed induction generator wind turbines using adaptive maximum power point tracking," Energy, Elsevier, vol. 111(C), pages 377-388.
    14. 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.
    15. Li, Qing’an & Maeda, Takao & Kamada, Yasunari & Mori, Naoya, 2017. "Investigation of wake effects on a Horizontal Axis Wind Turbine in field experiments (Part I: Horizontal axis direction)," Energy, Elsevier, vol. 134(C), pages 482-492.

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