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Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed

Author

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  • Anto Anbarasu Yesudhas

    (School of IT Information and Control Engineering, Kunsan National University, 588 Daehak-ro, Gunsan-si 54150, Jeonbuk, Korea)

  • Young Hoon Joo

    (School of IT Information and Control Engineering, Kunsan National University, 588 Daehak-ro, Gunsan-si 54150, Jeonbuk, Korea)

  • Seong Ryong Lee

    (School of IT Information and Control Engineering, Kunsan National University, 588 Daehak-ro, Gunsan-si 54150, Jeonbuk, Korea)

Abstract

Over the last few years, improving power extraction from the wind energy conversion system (WECS) under varying wind speeds has become a complex task. The current study presents the optimum maximum power point tracking (MPPT) control approach integrated with neural network (NN)-based rotor speed control and pitch angle control to extract the maximum power from the WECS. So, this study presents a reference model adaptive control (RMAC) for a direct-drive (DD) permanent magnet vernier generator (PMVG)-based WECS under real wind speed conditions. Initially, the RMAC-based rotor speed tracking control is presented with adaptive terms, which tracks a reference model that guarantees the expected exponential decay of rotor speed error trajectory. Then, to reduce the wind speed measurement errors, a recurrent neural network (RNN)-based training model is presented. Moreover, the asymptotic stability of the proposed control method is mathematically proven by Lyapunov theory. In addition, the pitch angle control is presented to efficiently operate the rotor speed within the allowable operating range. Eventually, the proposed control system demonstrates its effectiveness through simulation and experimentation using a prototype of 5 kW DD PMVG-based WECS. After that, the comparative results affirm the superiority of the proposed control method over existing control methods.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3091-:d:800522
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    References listed on IDEAS

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    1. 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.
    2. Yin, Xiuxing & Jiang, Zhansi & Pan, Li, 2020. "Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems," Renewable Energy, Elsevier, vol. 145(C), pages 1149-1157.
    3. Ramji Tiwari & Sanjeevikumar Padmanaban & Ramesh Babu Neelakandan, 2017. "Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System," Energies, MDPI, vol. 10(10), pages 1-17, September.
    4. Btissam Majout & Badre Bossoufi & Manale Bouderbala & Mehedi Masud & Jehad F. Al-Amri & Mohammed Taoussi & Mohammed El Mahfoud & Saad Motahhir & Mohammed Karim, 2022. "Improvement of PMSG-Based Wind Energy Conversion System Using Developed Sliding Mode Control," Energies, MDPI, vol. 15(5), pages 1-17, February.
    5. Chen, Jian & Yao, Wei & Zhang, Chuan-Ke & Ren, Yaxing & Jiang, Lin, 2019. "Design of robust MPPT controller for grid-connected PMSG-Based wind turbine via perturbation observation based nonlinear adaptive control," Renewable Energy, Elsevier, vol. 134(C), pages 478-495.
    6. Daekyu Jang & Junghwan Chang, 2017. "Influences of Winding MMF Harmonics on Torque Characteristics in Surface-Mounted Permanent Magnet Vernier Machines," Energies, MDPI, vol. 10(4), pages 1-17, April.
    7. Max A. Buettner & Niklas Monzen & Christoph M. Hackl, 2022. "Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art," Energies, MDPI, vol. 15(5), pages 1-38, March.
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