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Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability

Author

Listed:
  • Xuhong Yang

    (College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Hui Li

    (College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Wei Jia

    (Shanghai Institute of Space Power-Sources/State Key Laboratory of Space Power-Sources Technology, Shanghai 200245, China)

  • Zhongxin Liu

    (Shanghai Institute of Space Power-Sources/State Key Laboratory of Space Power-Sources Technology, Shanghai 200245, China)

  • Yu Pan

    (Shanghai Institute of Space Power-Sources/State Key Laboratory of Space Power-Sources Technology, Shanghai 200245, China)

  • Fengwei Qian

    (Shanghai Solar Energy Engineering Technology Research Center, Shanghai 200245, China)

Abstract

With the massive integration of renewable energy into the grid, grid inertia and its stability continue to decrease. To improve inertia and facilitate grid restoration, a control strategy for radial basis function virtual synchronous generators based on model predictive control (MPC-VSG-RBF) is proposed in this paper. In this method, virtual synchronous generator (VSG) control strategy is introduced into the model predictive control (MPC), so that the reference value of the inner loop current can vary with the grid voltage and frequency. Using the radial basis function (RBF) neural network to adjust the VSG virtual inertia online can solve the large fluctuation of frequency and power caused by excessive load fluctuation. The simulation model was built based on MATLAB and compared and analyzed with the MPC control method. The simulation results show that: when the output power of the inverter changes, the model predictive control of the adaptive virtual synchronous generator can increase the inertia and stability of the power grid; by adjusting the moment of inertia, the system damping ratio is improved to effectively suppress the transient process overshoot and oscillation in medium power.

Suggested Citation

  • Xuhong Yang & Hui Li & Wei Jia & Zhongxin Liu & Yu Pan & Fengwei Qian, 2022. "Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability," Energies, MDPI, vol. 15(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8385-:d:968041
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    References listed on IDEAS

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    1. Ajay Shetgaonkar & Aleksandra Lekić & José Luis Rueda Torres & Peter Palensky, 2021. "Microsecond Enhanced Indirect Model Predictive Control for Dynamic Power Management in MMC Units," Energies, MDPI, vol. 14(11), pages 1-26, June.
    2. Ariel Villalón & Marco Rivera & Yamisleydi Salgueiro & Javier Muñoz & Tomislav Dragičević & Frede Blaabjerg, 2020. "Predictive Control for Microgrid Applications: A Review Study," Energies, MDPI, vol. 13(10), pages 1-32, May.
    3. Lintao Ren & Hui Guo & Zhenlan Dou & Fei Wang & Lijun Zhang, 2022. "Modeling and Analysis of the Harmonic Interaction between Grid-Connected Inverter Clusters and the Utility Grid," Energies, MDPI, vol. 15(10), pages 1-19, May.
    4. Abdellatif Elmouatamid & Radouane Ouladsine & Mohamed Bakhouya & Najib El Kamoun & Mohammed Khaidar & Khalid Zine-Dine, 2020. "Review of Control and Energy Management Approaches in Micro-Grid Systems," Energies, MDPI, vol. 14(1), pages 1-30, December.
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    Cited by:

    1. Aleksey Suvorov & Alisher Askarov & Nikolay Ruban & Vladimir Rudnik & Pavel Radko & Andrey Achitaev & Konstantin Suslov, 2023. "An Adaptive Inertia and Damping Control Strategy Based on Enhanced Virtual Synchronous Generator Model," Mathematics, MDPI, vol. 11(18), pages 1-29, September.

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