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  • Long-horizon finite-set model predictive control for grid-connected photovoltaic inverters

    Author(s)
    Qiu, Quanwei
    Yang, Fuwen
    Zhu, Yong
    Han, Qing-Long
    Griffith University Author(s)
    Yang, Fuwen
    Zhu, Yong
    Year published
    2021
    Metadata
    Show full item record
    Abstract
    This article addresses the grid-connected single-phase photovoltaic (PV) inverter control. A long-horizon finite-set model predictive control (MPC) strategy is proposed to control the voltage source inverter. To achieve this, a multi-step implementation approach and a control sequence rearrangement method are designed to reduce the sampling frequency and switching frequency. The optimization problem for the finite-set MPC is further simplified to reduce the computational complexity of the optimization procedure. Moreover, a multi-step delay compensation method is developed to compensate for the computational delay of the ...
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    This article addresses the grid-connected single-phase photovoltaic (PV) inverter control. A long-horizon finite-set model predictive control (MPC) strategy is proposed to control the voltage source inverter. To achieve this, a multi-step implementation approach and a control sequence rearrangement method are designed to reduce the sampling frequency and switching frequency. The optimization problem for the finite-set MPC is further simplified to reduce the computational complexity of the optimization procedure. Moreover, a multi-step delay compensation method is developed to compensate for the computational delay of the control algorithm. Finally, the proposed control method is implemented in a grid-connected PV inverter and simulation test results demonstrate its effectiveness under different load and generation conditions.
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    Journal Title
    Optimal Control Applications and Methods
    DOI
    https://doi.org/10.1002/oca.2847
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Electrical engineering
    Science & Technology
    Physical Sciences
    Automation & Control Systems
    Operations Research & Management Science
    Publication URI
    http://hdl.handle.net/10072/411544
    Collection
    • Journal articles

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