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Data-Driven Virtual Inertia Control Method of Doubly Fed Wind Turbine

Author

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  • Tai Li

    (School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
    School of Electrical Information Engineering, Jiangsu University, Zhenjiang 212213, China
    Shandong Driving Thunder Technology Development Co., Ltd., Yantai 264001, China)

  • Leqiu Wang

    (School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China)

  • Yanbo Wang

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Guohai Liu

    (School of Electrical Information Engineering, Jiangsu University, Zhenjiang 212213, China)

  • Zhiyu Zhu

    (School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China)

  • Yongwei Zhang

    (School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China)

  • Li Zhao

    (Shandong Water Polytechnic, Department of Mechanical and Electrical Engineering, Rizhao 276826, China)

  • Zhicheng Ji

    (School of Internet of Things Engineering, Jiangnan University, Wuxi 214112, China)

Abstract

This paper presents a data-driven virtual inertia control method for doubly fed induction generator (DFIG)-based wind turbine to provide inertia support in the presence of frequency events. The Markov parameters of the system are first obtained by monitoring the grid frequency and system operation state. Then, a data-driven state observer is developed to evaluate the state vector of the optimal controller. Furthermore, the optimal controller of the inertia emulation system is developed through the closed solution of the differential Riccati equation. Moreover, a differential Riccati equation with self-correction capability is developed to enhance the anti-noise ability to reject noise interference in frequency measurement process. Finally, the simulation verification was performed in Matlab/Simulink to validate the effectiveness of the proposed control strategy. Simulation results showed that the proposed virtual inertia controller can adaptively tune control parameters online to provide transient inertia supports for the power grid by releasing the kinetic energy, so as to improve the robustness and anti-interference ability of the control system of the wind power system.

Suggested Citation

  • Tai Li & Leqiu Wang & Yanbo Wang & Guohai Liu & Zhiyu Zhu & Yongwei Zhang & Li Zhao & Zhicheng Ji, 2021. "Data-Driven Virtual Inertia Control Method of Doubly Fed Wind Turbine," Energies, MDPI, vol. 14(17), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5572-:d:629964
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    References listed on IDEAS

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    1. Pradhan, Chittaranjan & Bhende, Chandrashekhar Narayan & Samanta, Anik Kumar, 2018. "Adaptive virtual inertia-based frequency regulation in wind power systems," Renewable Energy, Elsevier, vol. 115(C), pages 558-574.
    2. He, Zhengxia & Xu, Shichun & Shen, Wenxing & Long, Ruyin & Yang, He, 2016. "Overview of the development of the Chinese Jiangsu coastal wind-power industry cluster," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 59-71.
    3. Tiejiang Yuan & Jinjun Wang & Yuhang Guan & Zheng Liu & Xinfu Song & Yong Che & Wenping Cao, 2018. "Virtual Inertia Adaptive Control of a Doubly Fed Induction Generator (DFIG) Wind Power System with Hydrogen Energy Storage," Energies, MDPI, vol. 11(4), pages 1-16, April.
    4. Christina N. Papadimitriou & Nicholas A. Vovos, 2010. "Transient Response Improvement of Microgrids Exploiting the Inertia of a Doubly-Fed Induction Generator (DFIG)," Energies, MDPI, vol. 3(6), pages 1-18, June.
    5. Wei Gu & Wei Liu & Zhi Wu & Bo Zhao & Wu Chen, 2013. "Cooperative Control to Enhance the Frequency Stability of Islanded Microgrids with DFIG-SMES," Energies, MDPI, vol. 6(8), pages 1-21, August.
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    Cited by:

    1. Zbigniew Skibko & Grzegorz Hołdyński & Andrzej Borusiewicz, 2022. "Impact of Wind Power Plant Operation on Voltage Quality Parameters—Example from Poland," Energies, MDPI, vol. 15(15), pages 1-16, August.
    2. Mehreen Saleem Gul & Hassam Nasarullah Chaudhry, 2022. "Energy Efficiency, Low Carbon Resources and Renewable Technology," Energies, MDPI, vol. 15(13), pages 1-3, June.
    3. Abdel-Raheem Youssef & Mohamad Mallah & Abdelfatah Ali & Mostafa F. Shaaban & Essam E. M. Mohamed, 2023. "Enhancement of Microgrid Frequency Stability Based on the Combined Power-to-Hydrogen-to-Power Technology under High Penetration Renewable Units," Energies, MDPI, vol. 16(8), pages 1-18, April.
    4. Tai Li & Yanbo Wang & Sunan Sun & Huimin Qian & Leqiu Wang & Lei Wang & Yanxia Shen & Zhicheng Ji, 2023. "Fuzzy Active Disturbance Rejection-Based Virtual Inertia Control Strategy for Wind Farms," Energies, MDPI, vol. 16(10), pages 1-16, May.

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