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Model Predictive Control for Virtual Synchronous Generator with Improved Vector Selection and Reconstructed Current

Author

Listed:
  • Nan Jin

    (School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China)

  • Chao Pan

    (School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China)

  • Yanyan Li

    (School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China)

  • Shiyang Hu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Jie Fang

    (School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China)

Abstract

Due to the large-scale renewable energy connected to the power grid by power electronic converters, the inertia and stability of the power grid is declining. In order to improve the inertia and support the grid recovery, the three-phase converter works as a virtual synchronous generator (VSG) to respond to the frequency and voltage changes of the power grid. This paper proposes a model predictive control for the virtual synchronous generator (MPC-VSG) strategy, which can automatically control the converter output power with the grid frequency and voltage changes. Further consideration of fault-tolerant ability and reliability, the method based on improved voltage vector selection, and reconstructed current is used for MPC-VSG to ensure continuous operation for three-phase converters that have current-sensor faults, and improve the reconstruction precision. The proposed method can respond to the frequency and voltage changes of the power grid and has fault-tolerant ability, which is easy to realize without pulse width modulation (PWM) and a proportional-integral (PI) controller. The effectiveness of the proposed control strategy is verified by experiment.

Suggested Citation

  • Nan Jin & Chao Pan & Yanyan Li & Shiyang Hu & Jie Fang, 2020. "Model Predictive Control for Virtual Synchronous Generator with Improved Vector Selection and Reconstructed Current," Energies, MDPI, vol. 13(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5435-:d:430789
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    Citations

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    Cited by:

    1. Yalin Liang & Yuyao He & Yun Niu, 2022. "Robust Errorless-Control-Targeted Technique Based on MPC for Microgrid with Uncertain Electric Vehicle Energy Storage Systems," Energies, MDPI, vol. 15(4), pages 1-23, February.
    2. Silvio Simani & Elena Zattoni, 2021. "Advanced Control Design and Fault Diagnosis," Energies, MDPI, vol. 14(18), pages 1-6, September.
    3. Thyago Estrabis & Gabriel Gentil & Raymundo Cordero, 2021. "Development of a Resolver-to-Digital Converter Based on Second-Order Difference Generalized Predictive Control," Energies, MDPI, vol. 14(2), pages 1-22, January.
    4. Zhiming Liao & Tianran Peng & Jia Liu & Tao Guo, 2023. "Multi-Adjustment Strategy for Phase Current Reconstruction of Permanent Magnet Synchronous Motors Based on Model Predictive Control," Energies, MDPI, vol. 16(15), pages 1-16, July.
    5. Paolo Mercorelli, 2022. "Model Predictive Control for Energy Optimization in Generators/Motors as Well as Converters and Inverters for Futuristic Integrated Power Networks," Energies, MDPI, vol. 15(16), pages 1-4, August.
    6. João Inácio Da Silva Filho & Raphael Adamelk Bispo de Oliveira & Marcos Carneiro Rodrigues & Hyghor Miranda Côrtes & Alexandre Rocco & Mauricio Conceição Mario & Dorotéa Vilanova Garcia & Jair Minoro , 2023. "Predictive Controller Based on Paraconsistent Annotated Logic for Synchronous Generator Excitation Control," Energies, MDPI, vol. 16(4), pages 1-25, February.
    7. Jaime A. Rohten & Javier E. Muñoz & Esteban S. Pulido & José J. Silva & Felipe A. Villarroel & José R. Espinoza, 2021. "Very Low Sampling Frequency Model Predictive Control for Power Converters in the Medium and High-Power Range Applications," Energies, MDPI, vol. 14(1), pages 1-18, January.

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