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Enhancing secondary recovery in microgrid: Event-driven robust security control with semi-Markov switched dynamics

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  • Liu, Yu-An
  • Zhang, Li

Abstract

The secondary control of islanded microgrids holds paramount importance for maintaining power quality in smart grid regions, posing a significant challenge in recent research on multi-agent systems. This paper presents an improved secondary control approach designed in AC islanded microgrids (MGs) operating in a communication network environment. The proposed method first considers that the communication topology of distributed generation (DG) is switched randomly, and such switching is described by the semi-Markov process with an uncertain transition rate. Then, an asynchronous mechanism is used to correlate the evolution of system and controller modes in response to solve the difficulty of obtaining modal information. Based on the aforementioned foundation, an event-based distributed security secondary control algorithm is constructed, leveraging feedback linearization and the leader-follower consensus protocol. Specifically, a hybrid event-triggered mechanism, characterized by both memorability and dynamics, is introduced, effectively mitigating communication load. Furthermore, by taking the false-data-injection attack as a case study, the algorithm enhances the security of communication control. Besides, considering the limitation of model error and the influence of external noise, a sliding mode control is additionally applied to enhance the control robustness among the secondary control layer. The proposed method is verified and evaluated from theoretical and emulational perspectives. The research findings demonstrate that the suggested secondary control has robust performance for the environment constraint while satisfying the targeted frequency recovery and real power allocation.

Suggested Citation

  • Liu, Yu-An & Zhang, Li, 2024. "Enhancing secondary recovery in microgrid: Event-driven robust security control with semi-Markov switched dynamics," Applied Mathematics and Computation, Elsevier, vol. 478(C).
    • Handle: RePEc:eee:apmaco:v:478:y:2024:i:c:s0096300324002996
    DOI: 10.1016/j.amc.2024.128838
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    References listed on IDEAS

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    1. Sun, Lu & Xu, Qingshan & Song, Yun, 2022. "Game-theoretic genetic-priced optimization of multiple microgrids under uncertainties," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    2. Ding, Hongfei & Wang, Yudong & Shen, Hao, 2024. "A reinforcement learning integral sliding mode control scheme against lumped disturbances in hot strip rolling," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    3. Liu, Xinrui & Zhang, Mingchao & Xie, Xiangpeng & Zhao, Liang & Sun, Qiuye, 2022. "Consensus-based energy management of multi-microgrid: An improved SoC-based power coordinated control method," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    4. Wu, Shuang & Ye, Dan & Shao, Xinfeng, 2023. "Event-based distributed resilient control strategy for microgrids subject to disturbances and hybrid attacks," Applied Mathematics and Computation, Elsevier, vol. 459(C).
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