The technology of virtual synchronous machine (VSM) is attracting interest of researchers as it controls converters mimicking the synchronous machine’s response so as to provide inertial support for power electronics dominated smart grids. For the VSM-based microgrid, its slow dynamics are dominated by synchronous generators (SGs) and the VSM control loops, which makes it possible to model this microgrid into an equivalent SG (EqSG) model. This paper proposes a robust secondary frequency control design method for the VSM-based low voltage (LV) microgrid cluster (MGC) using equivalent modeling. The EqSG model is used to construct the MGC model so as to reduce the model order and the complexity of controller synthesis. Modeling errors caused by the EqSG model and different operating conditions are integrated into the MGC model as unstructured uncertainties. The proposed secondary frequency control strategy is based on the distributed-centralized hybrid control structure to coordinate frequency restoration among LV microgrids. Structured μ-synthesis is applied for tuning control parameters realizing H∞ robust performance against unstructured uncertainties. To reduce the communication resource consumption, an event-triggered mechanism considering communication delay is introduced in the robust secondary frequency control strategy. The triggering condition is analyzed using a Lyapunov function to guarantee H∞ robust stability. Simulation and real-time experiment results on a MGC composed of four CIGRÉ benchmark LV microgrids are presented to demonstrate the effectiveness of the proposed control strategy.


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    Titel :

    Robust Secondary Frequency Control for Virtual Synchronous Machine-Based Microgrid Cluster Using Equivalent Modeling


    Beteiligte:
    Hu, Wenqiang (Autor:in) / Wu, Zaijun (Autor:in) / Lv, Xinxin (Autor:in) / Dinavahi, Venkata (Autor:in)

    Erscheinungsdatum :

    01.07.2021


    Anmerkungen:

    oai:zenodo.org:7694229



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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