A

Distributed Optimal Synchronization Rate Control for AC Microgrids Under Event-Triggered Mechanism

IEEE Transactions on Power Systems, vol. 36, pp. 1780–1793

Abstract

With the development of distributed generation (DG) technologies, distributed energy resources (DERs) with low capacity and low inertia are highly penetrated in the islanded AC microgrid. This has highlighted the need for secondary control strategies to remedy frequency deviation and strongly limitation of the frequency synchronization rate, which are associated with the primary control. To solve this problem, an optimal condition, in terms of explicit synchronization rate formula, is derived. Then, a distributed event-triggered control strategy is proposed to synchronize the microgrid frequency to the nominal value and maximize the synchronization rate for the primary control process. To reduce the communication and computation burdens, an event-triggered mechanism that enables each agent to update its input based on discrete information from only one of its neighbors are provided. The stability of the event condition and event interval are also analyzed using Lyapunov method. Finally, the theoretical results are applied to a parallel-feeder test system consisting of fourteen DGs, which verifies the effectiveness of the proposed strategy.

Authors 5

  1. Wuhan University

    Affiliation as printed

    School of Electrical Engineering and Automation, Wuhan University, Wuhan, China

  2. Wuhan University

    Affiliation as printed

    Department of Artificial Intelligence and Automation, Wuhan University, Wuhan, China

  3. Wuhan University

    Affiliation as printed

    School of Electrical Engineering and Automation, Wuhan University, Wuhan, China

  4. Huazhong University of Science and Technology · RWTH Aachen University

    Affiliation as printed

    E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany

    School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China

  5. Wuhan University

    Affiliation as printed

    Department of Artificial Intelligence and Automation, Wuhan University, Wuhan, China

Cited by 32 stored of 32

No patents citing this paper on Lens.org (checked 2026-10-06).

References 31