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An MPPT Strategy for Wind Turbines Combining Feedback Linearization and Model Predictive Control

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  • Ping Jiang

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China
    Baoding Key Laboratory of Digital Intelligent Operation and Maintenance of Wind Power Generation, Hebei University, Baoding 071002, China)

  • Tianyi Zhang

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China
    Baoding Key Laboratory of Digital Intelligent Operation and Maintenance of Wind Power Generation, Hebei University, Baoding 071002, China)

  • Jinpeng Geng

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China
    Baoding Key Laboratory of Digital Intelligent Operation and Maintenance of Wind Power Generation, Hebei University, Baoding 071002, China)

  • Peiguang Wang

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China)

  • Lei Fu

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China)

Abstract

This paper proposes a model predictive controller (MPC) design based on the optimal tip-speed ratio method for maximum power point tracking (MPPT) of a direct-driven permanent magnet synchronous generator (D-PMSG)-based wind energy conversion system (WECS). To eliminate system nonlinearity and time-varying characteristics, a control variable was added at the wind turbine and the system model was feedback-linearized to create a linear time-invariant system, reducing the computational burden of the MPC and improving system performance. MATLAB/Simulink simulations were performed and the results show that the linearized system has high fidelity. Compared to traditional MPC that use an operating point to linearize the system, it has better adaptability to turbulent wind speeds, improving the stability and rapidity of the system.

Suggested Citation

  • Ping Jiang & Tianyi Zhang & Jinpeng Geng & Peiguang Wang & Lei Fu, 2023. "An MPPT Strategy for Wind Turbines Combining Feedback Linearization and Model Predictive Control," Energies, MDPI, vol. 16(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4244-:d:1152653
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    References listed on IDEAS

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    1. 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.
    2. Nerg, Janne & Ruuskanen, Vesa, 2013. "Lumped-parameter-based thermal analysis of a doubly radial forced-air-cooled direct-driven permanent magnet wind generator," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 90(C), pages 218-229.
    3. Krishna, V.B Murali & Sandeep, V. & Murthy, S.S. & Yadlapati, Kishore, 2022. "Experimental investigation on performance comparison of self excited induction generator and permanent magnet synchronous generator for small scale renewable energy applications," Renewable Energy, Elsevier, vol. 195(C), pages 431-441.
    4. Grenier, Damien & Louis, Jean-Paul, 1995. "Modeling for control of non-sinewave permanent-magnet synchronous drives by extending Park's transformation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 38(4), pages 445-452.
    5. Narayana, Mahinsasa & Sunderland, Keith M. & Putrus, Ghanim & Conlon, Michael F., 2017. "Adaptive linear prediction for optimal control of wind turbines," Renewable Energy, Elsevier, vol. 113(C), pages 895-906.
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    Cited by:

    1. Huajun Ran & Linwei Li & Ao Li & Xinquan Wang, 2024. "Enhanced MPPT in Permanent Magnet Direct-Drive Wind Power Systems via Improved Sliding Mode Control," Energies, MDPI, vol. 17(18), pages 1-17, September.

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