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Adaptive Backstepping Control with Online Parameter Estimator for a Plug-and-Play Parallel Converter System in a Power Switcher

Author

Listed:
  • Chujia Guo

    (School of Electronic and Information Engineering, Xi’an Jiaotong University, No. 28, West Xianning Road, Xi’an 710049, China)

  • Aimin Zhang

    (School of Electronic and Information Engineering, Xi’an Jiaotong University, No. 28, West Xianning Road, Xi’an 710049, China)

  • Hang Zhang

    (School of Electrical Engineering, Xi’an Jiaotong University, No. 28, West Xianning Road, Xi’an 710049, China)

  • Lei Zhang

    (School of Electronics and Information, Xi’an Polytechnic University, No. 19, South Jinhua Road, Xi’an 710049, China)

Abstract

This study aims to address the inherent uncertainty in plug loads and load extraction, distributed generation, and the inevitable circulating current in a parallel structure. Therefore, in this paper, an adaptive backstepping control scheme with an online parameter estimator (OPE) for a plug-and-play parallel converter system in a four-port power switcher is proposed. The adaptive backstepping control method was designed in the dq0 coordinate system to suppress the circulating current in the zero-component; the circulating current can be suppressed by using an embedded algorithm and omitting the extra controller. An adaptive update law was designed to weaken the influence of the arbitrary plug and extraction operations in the DC and AC buses to realize the plug-and-play function. The transient tracking performance is governed by the limitation of maximum total errors in the voltage and current. As a result, the settling times of the voltage, current, and power decreased. Additionally, to further improve the system robustness, an online inductance and resistance estimator was established using an optimal algorithm that solves the weighted least squares problem. In the estimator, there are no additional voltage and current sensors needed, and the mean squared error (MSE) of the estimation can be minimized. Simulation studies on a two-converter parallel system with a plug-and-play function were conducted using MATLAB/SIMULINK (R2018b, MathWorks, Natick, MA, USA) to verify the effectiveness of the proposed adaptive backstepping control strategy. The results show that this strategy improves system performance over that of a system with unbalanced parameters among a parallel structure with AC and DC system disturbances caused by arbitrary plug and extraction operations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3528-:d:191449
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    References listed on IDEAS

    as
    1. Qingyu Su & Fei Dong & Xueqiang Shen, 2018. "Improved Adaptive Backstepping Sliding Mode Control of Static Var Compensator," Energies, MDPI, vol. 11(10), pages 1-12, October.
    2. Majid Ghaffarianfar & Amin Hajizadeh, 2018. "Voltage Stability of Low-Voltage Distribution Grid with High Penetration of Photovoltaic Power Units," Energies, MDPI, vol. 11(8), pages 1-13, July.
    3. Weipeng Yang & Hang Zhang & Jungang Li & Aimin Zhang & Yunhong Zhou & Jianhua Wang, 2018. "PIDR Sliding Mode Current Control with Online Inductance Estimator for VSC-MVDC System Converter Stations under Unbalanced Grid Voltage Conditions," Energies, MDPI, vol. 11(10), pages 1-20, September.
    4. 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.
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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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