Skip to main content

ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1362412

Campus Microgrid Protection: A Unified Approach Against Cyberattacks

Provisionally accepted
Huanan Liu Huanan Liu 1*Long Huang Long Huang 1Zhenlan Dou Zhenlan Dou 2Songcen Wang Songcen Wang 3
  • 1 School of Information Engineering, Nanchang University, Nanchang, China
  • 2 State Grid Shanghai Municupal Electric Power Company, Shanghai, China
  • 3 China Electric Power Research Institute (CEPRI), Beijing, China

The final, formatted version of the article will be published soon.

    When False Data Injection (FDI) attacks inject false data into the controllers of a microgrid, it can lead the controllers to make decisions based on inaccurate information, ultimately resulting in system collapse. This study presents a novel approach to dynamically adjust output voltage in Campus Microgrids (CMG). Initially, we employ feedback linearization techniques to address the inherent nonlinear complexities in Distributed Generation (DG) dynamics. Moreover, during voltage regulation, controllers and sensors may be susceptible to FDI attacks. To address this, an elastic follower observer is introduced to mitigate the impact of false information injection attacks on voltage coordination, thus ensuring the robustness and stability of the system. Notably, our method reduces dependence on global information and fault boundaries within the communication network, thereby enhancing system elasticity and efficiency. In conclusion, through comprehensive simulation experiments, we evaluate the effectiveness and performance of our proposed strategy. The simulation results validate the success of our approach in voltage regulation and its efficiency in fault management. These findings bear significant implications for the advancement of voltage regulation strategies in distributed generation systems.

    Keywords: Voltage regulation control, fully distributed control, cyberattacks, Distributed generation (DG), False data injection (FDI)

    Received: 28 Dec 2023; Accepted: 30 May 2024.

    Copyright: © 2024 Liu, Huang, Dou and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Huanan Liu, School of Information Engineering, Nanchang University, Nanchang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.