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Research on the Operation Control Strategy of a Low-Voltage Direct Current Microgrid Based on a Disturbance Observer and Neural Network Adaptive Control Algorithm

Author

Listed:
  • Liang Zhang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Kang Chen

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Ling Lyu

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Guowei Cai

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

Abstract

Low-voltage direct current (DC) microgrid based on distributed generation (DG), the problems of load mutation affecting the DC bus under island mode, and the security problems that may arise when the DC microgrid is switched from island mode to grid-connected mode are considered. Firstly, a DC bus control algorithm based on disturbance observer (DOB) was proposed to suppress the impact of system load mutation on DC bus in island mode. Then, in a grid-connected mode, a pre-synchronization control algorithm based on a neural network adaptive control was proposed, and the droop controller was improved to ensure better control accuracy. Through this pre-synchronization control, the microgrid inverters output voltage could quickly track the power grid’s voltage and achieve an accurate grid-connected operation. The effectiveness of the algorithms was verified by simulation.

Suggested Citation

  • Liang Zhang & Kang Chen & Ling Lyu & Guowei Cai, 2019. "Research on the Operation Control Strategy of a Low-Voltage Direct Current Microgrid Based on a Disturbance Observer and Neural Network Adaptive Control Algorithm," Energies, MDPI, vol. 12(6), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1162-:d:217103
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    References listed on IDEAS

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    1. Yuxia Jiang & Yonggang Li & Yanjun Tian & Luo Wang, 2018. "Phase-Locked Loop Research of Grid-Connected Inverter Based on Impedance Analysis," Energies, MDPI, vol. 11(11), pages 1-21, November.
    2. Rahmani-Andebili, Mehdi, 2017. "Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization," Renewable Energy, Elsevier, vol. 113(C), pages 1462-1471.
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    Cited by:

    1. Alfredo Padilla-Medina & Francisco Perez-Pinal & Alonso Jimenez-Garibay & Antonio Vazquez-Lopez & Juan Martinez-Nolasco, 2020. "Design and Implementation of an Energy-Management System for a Grid-Connected Residential DC Microgrid," Energies, MDPI, vol. 13(16), pages 1-30, August.
    2. Miloud Rezkallah & Sanjeev Singh & Ambrish Chandra & Bhim Singh & Hussein Ibrahim, 2020. "Off-Grid System Configurations for Coordinated Control of Renewable Energy Sources," Energies, MDPI, vol. 13(18), pages 1-25, September.
    3. Yao Liu & Lin Guan & Fang Guo & Jianping Zheng & Jianfu Chen & Chao Liu & Josep M. Guerrero, 2019. "A Reactive Power-Voltage Control Strategy of an AC Microgrid Based on Adaptive Virtual Impedance," Energies, MDPI, vol. 12(16), pages 1-15, August.
    4. Óscar Gonzales-Zurita & Jean-Michel Clairand & Elisa Peñalvo-López & Guillermo Escrivá-Escrivá, 2020. "Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids," Energies, MDPI, vol. 13(13), pages 1-29, July.
    5. Kunyu Dong & Jiaoxin Jia, 2023. "A Pre-Synchronization Method for Parallel VSGs of Distributed Microgrid Based on Control Mode Switching," Energies, MDPI, vol. 16(10), pages 1-13, May.

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