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Adaptive Command-Filtered Backstepping Control for Virtual Synchronous Generators

Author

Listed:
  • Chengshun Yang

    (Nanjing Institute of Technology, School of Electric Power Engineering, Nanjing 211167, China)

  • Fan Yang

    (Nanjing Institute of Technology, School of Electric Power Engineering, Nanjing 211167, China)

  • Dezhi Xu

    (School of IoT Engineering, Jiangnan University, Wuxi 214122, China)

  • Xiaoning Huang

    (Nanjing Institute of Technology, School of Electric Power Engineering, Nanjing 211167, China)

  • Dongdong Zhang

    (Nanjing Institute of Technology, School of Electric Power Engineering, Nanjing 211167, China)

Abstract

Distributed energy sources are usually interfaced to the grid using power electronic converters, and lack of inertia in inverter dominated microgrids can affect the system stability. This paper presents a new method for virtual synchronous generator (VSG) control in order to solve the low system inertia and support the grid frequency problem. In this paper, the VSG based on electromagnetic transient characteristics is improved and an adaptive command filter back-stepping controller is designed. Firstly, the rotor swing equation and power part are modeled to complete the controller design for achieving system stability in the islanded, grid-connected and transition modes. In addition, a limited-amplitude command filter is used to deal with computational complexity and nonlinear saturation problems in the design process. Secondly, projection operator, and adaptive inertia and damping control are introduced to reduce the modeling error and disturbance caused by changing parameters. This ensures the boundedness of the estimated value and further improves the frequency response, especially in the transition mode. Finally, simulation results show that the proposed controller is more effective than the traditional control method for achieving power stability and frequency improvement.

Suggested Citation

  • Chengshun Yang & Fan Yang & Dezhi Xu & Xiaoning Huang & Dongdong Zhang, 2019. "Adaptive Command-Filtered Backstepping Control for Virtual Synchronous Generators," Energies, MDPI, vol. 12(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2681-:d:247811
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    References listed on IDEAS

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    1. Ting Yang & Yajian Zhang & Zhaoxia Wang & Haibo Pen, 2018. "Secondary Frequency Stochastic Optimal Control in Independent Microgrids with Virtual Synchronous Generator-Controlled Energy Storage Systems," Energies, MDPI, vol. 11(9), pages 1-14, September.
    2. Di Hu & Ming Ding & Lei Sun & Jingjing Zhang, 2019. "Planning of High Renewable-Penetrated Distribution Systems Considering Complementarity and Cluster Partitioning," Energies, MDPI, vol. 12(11), pages 1-22, May.
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    Citations

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    Cited by:

    1. Chih-Hong Lin, 2020. "Permanent-Magnet Synchronous Motor Drive System Using Backstepping Control with Three Adaptive Rules and Revised Recurring Sieved Pollaczek Polynomials Neural Network with Reformed Grey Wolf Optimizat," Energies, MDPI, vol. 13(22), pages 1-33, November.
    2. Der-Fa Chen & Yi-Cheng Shih & Shih-Cheng Li & Chin-Tung Chen & Jung-Chu Ting, 2020. "Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO," Energies, MDPI, vol. 13(11), pages 1-25, June.
    3. Chih-Hong Lin, 2020. "A Rectified Reiterative Sieved-Pollaczek Polynomials Neural Network Backstepping Control with Improved Fish School Search for Motor Drive System," Mathematics, MDPI, vol. 8(10), pages 1-34, October.

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