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Disturbance-Observer-Based Model Predictive Control for Battery Energy Storage System Modular Multilevel Converters

Author

Listed:
  • Yantao Liao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Jun You

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Jun Yang

    (Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China)

  • Zuo Wang

    (Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China)

  • Long Jin

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

Although the traditional model predictive control (MPC) can theoretically provide AC current and circulating current control for modular multilevel converters (MMCs) in battery energy storage grid-connected systems, it suffers from stability problems due to the power quality of the power grid and model parameter mismatches. A two discrete-time disturbance observers (DOBs)-based MPC strategy is investigated in this paper to solve this problem. The first DOB is used to improve the AC current quality and the second enhances the stability of the circulating current control. The distortion and fluctuation of grid voltage and inductance parameter variation are considered as lump disturbances in the discrete modeling of a MMC. Based on the proposed method, the output prediction is compensated by disturbance estimation to correct the AC current and circulating current errors, which eventually achieve the expected tracking performance. Moreover, the DOBs have a quite low computational cost with minimum order and optimal performance properties. Since the designed DOBs work in parallel with the MPC, the control effect is improved greatly under harmonics, 3-phase unbalance, voltage sag, inductance parameter mismatches and power reversal conditions. Simulation results confirm the validity of the proposed scheme.

Suggested Citation

  • Yantao Liao & Jun You & Jun Yang & Zuo Wang & Long Jin, 2018. "Disturbance-Observer-Based Model Predictive Control for Battery Energy Storage System Modular Multilevel Converters," Energies, MDPI, vol. 11(9), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2285-:d:166698
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    References listed on IDEAS

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    1. Junhong Song & Weige Zhang & Hui Liang & Jiuchun Jiang & Wensong Yu, 2018. "Fault-Tolerant Control for a Flexible Group Battery Energy Storage System Based on Cascaded Multilevel Converters," Energies, MDPI, vol. 11(1), pages 1-19, January.
    2. Hui Liang & Long Guo & Junhong Song & Yong Yang & Weige Zhang & Hongfeng Qi, 2018. "State-of-Charge Balancing Control of a Modular Multilevel Converter with an Integrated Battery Energy Storage," Energies, MDPI, vol. 11(4), pages 1-18, April.
    3. Majid Mehrasa & Edris Pouresmaeil & Sasan Zabihi & Juan C. Trujillo Caballero & João P. S. Catalão, 2016. "A Novel Modulation Function-Based Control of Modular Multilevel Converters for High Voltage Direct Current Transmission Systems," Energies, MDPI, vol. 9(11), pages 1-14, October.
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    Cited by:

    1. Roberto Zanasi & Davide Tebaldi, 2021. "Modeling Control and Robustness Assessment of Multilevel Flying-Capacitor Converters," Energies, MDPI, vol. 14(7), pages 1-40, March.
    2. Chujia Guo & Aimin Zhang & Hang Zhang & Lei Zhang, 2018. "Adaptive Backstepping Control with Online Parameter Estimator for a Plug-and-Play Parallel Converter System in a Power Switcher," Energies, MDPI, vol. 11(12), pages 1-18, December.

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