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Virtual Synchronous Generator, a Comprehensive Overview

Author

Listed:
  • Wenju Sang

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Wenyong Guo

    (Center for Applied Superconductivity, School of Electrical Engineering, Beijing Jiao Tong University, Beijing 100044, China)

  • Shaotao Dai

    (Center for Applied Superconductivity, School of Electrical Engineering, Beijing Jiao Tong University, Beijing 100044, China)

  • Chenyu Tian

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Suhang Yu

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yuping Teng

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

Renewable energy sources (RESs) are generally connected to the grid through power electronic interfaces, which generate electrical power instantaneously with little inertia. With the increasing penetration of RESs, the grid will gradually develop into a low inertia and underdamped power system, which results in serious grid frequency stabilization problems. The virtual synchronous generator (VSG) is an emerging technology that mimics the operation characteristics of traditional synchronous generators (SGs). Virtual inertia and damping are therefore introduced, which help to stabilize grid frequency. This paper gives a comprehensive overview of the VSG. The basic operation principle of VSG is introduced and analyzed in depth. The key issues related to VSG are summarized and discussed, including hardware configuration, software control strategies, energy supporting methods, and typical applications.

Suggested Citation

  • Wenju Sang & Wenyong Guo & Shaotao Dai & Chenyu Tian & Suhang Yu & Yuping Teng, 2022. "Virtual Synchronous Generator, a Comprehensive Overview," Energies, MDPI, vol. 15(17), pages 1-29, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6148-:d:896491
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    References listed on IDEAS

    as
    1. Quan-Quan Zhang & Rong-Jong Wai, 2021. "Robust Power Sharing and Voltage Stabilization Control Structure via Sliding-Mode Technique in Islanded Micro-Grid," Energies, MDPI, vol. 14(4), pages 1-27, February.
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    Cited by:

    1. Solomon Feleke & Balamurali Pydi & Raavi Satish & Hossam Kotb & Mohammed Alenezi & Mokhtar Shouran, 2023. "Frequency Stability Enhancement Using Differential-Evolution- and Genetic-Algorithm-Optimized Intelligent Controllers in Multiple Virtual Synchronous Machine Systems," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    2. Daniele Linaro & Federico Bizzarri & Davide Giudice & Cosimo Pisani & Giorgio M. Giannuzzi & Samuele Grillo & Angelo M. Brambilla, 2023. "Continuous estimation of power system inertia using convolutional neural networks," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Myada Shadoul & Razzaqul Ahshan & Rashid S. AlAbri & Abdullah Al-Badi & Mohammed Albadi & Mohsin Jamil, 2022. "A Comprehensive Review on a Virtual-Synchronous Generator: Topologies, Control Orders and Techniques, Energy Storages, and Applications," Energies, MDPI, vol. 15(22), pages 1-27, November.
    4. Rongliang Shi & Caihua Lan & Ji Huang & Chengwei Ju, 2023. "Analysis and Optimization Strategy of Active Power Dynamic Response for VSG under a Weak Grid," Energies, MDPI, vol. 16(12), pages 1-18, June.
    5. Kabir Momoh & Shamsul Aizam Zulkifli & Petr Korba & Felix Rafael Segundo Sevilla & Arif Nur Afandi & Alfredo Velazquez-Ibañez, 2023. "State-of-the-Art Grid Stability Improvement Techniques for Electric Vehicle Fast-Charging Stations for Future Outlooks," Energies, MDPI, vol. 16(9), pages 1-29, May.
    6. Grzegorz Drałus & Damian Mazur & Jacek Kusznier & Jakub Drałus, 2023. "Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation," Energies, MDPI, vol. 16(18), pages 1-23, September.

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