Prescribed-Time Mitigation of False Data Attacks in Microgrid Load Frequency Control

Microgrid load frequency control (LFC) operation relies extensively on cyber networks which significantly increases their susceptibility to false data injection attacks (FDIAs) that can undermine microgrid’s stability. This paper proposes a multi-layer control framework that efficiently detects and mitigates false data injected in the actuator and measurements of the LFC. Firstly, an FDIAs detection layer based on an unknown input observer (UIO) utilizing an adaptive threshold has been proposed. An adaptive threshold aims to enhance detection sensitivity and reduce false alarms by distinguishing between actual attacks and temporal discrepancies in observer error caused by internal disturbances. Secondly, to ensure timely and accurate mitigation of FDIAs, an estimation layer utilizing a prescribed-time unknown input observer (PTUIO) has been designed. PTUIO is designed to be robust against unknown inputs and parameter uncertainty. The PTUIO employs periodic time-delayed output to estimate system states and FDIAs within a predefined time. Afterward, a two-layer robust and resilient LFC system was developed. The first layer ensures the rejection of unknown inputs, including load/generation disturbances and FDIAs based on PTUIO estimations. The second layer incorporates robust state feedback control, which stabilizes the microgrid and accurately tracks the frequency reference. The effectiveness of the proposed control system is confirmed through multiple scenarios by simulations and Hardware-in-the-loop (HIL), demonstrating its robustness and reliability in addressing challenges associated with internal disturbances, and FDIAs within the microgrid.

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Publication Details

Journal
IEEE Transactions on Smart Grid
Published
2025-04-02
DOI
https://doi.org/10.1109/tsg.2025.3557207
Citations
2
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
2.43
Controls
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article

Prescribed-Time Mitigation of False Data Attacks in Microgrid Load Frequency Control

Mohamed Zaery, Esam H. Abdelhameed, Said I. Abouzeid, Syed Muhammad Amrr et al.
2 citations
IEEE Transactions on Smart Grid
Smart Grid Security and Resilience
2.43
article

Prescribed-Time Mitigation of False Data Attacks in Microgrid Load Frequency Control

Mohamed Zaery, Esam H. Abdelhameed, Said I. Abouzeid, Syed Muhammad Amrr, Yong Chen
article en
2 citations

Abstract

Microgrid load frequency control (LFC) operation relies extensively on cyber networks which significantly increases their susceptibility to false data injection attacks (FDIAs) that can undermine microgrid’s stability. This paper proposes a multi-layer control framework that efficiently detects and mitigates false data injected in the actuator and measurements of the LFC. Firstly, an FDIAs detection layer based on an unknown input observer (UIO) utilizing an adaptive threshold has been proposed. An adaptive threshold aims to enhance detection sensitivity and reduce false alarms by distinguishing between actual attacks and temporal discrepancies in observer error caused by internal disturbances. Secondly, to ensure timely and accurate mitigation of FDIAs, an estimation layer utilizing a prescribed-time unknown input observer (PTUIO) has been designed. PTUIO is designed to be robust against unknown inputs and parameter uncertainty. The PTUIO employs periodic time-delayed output to estimate system states and FDIAs within a predefined time. Afterward, a two-layer robust and resilient LFC system was developed. The first layer ensures the rejection of unknown inputs, including load/generation disturbances and FDIAs based on PTUIO estimations. The second layer incorporates robust state feedback control, which stabilizes the microgrid and accurately tracks the frequency reference. The effectiveness of the proposed control system is confirmed through multiple scenarios by simulations and Hardware-in-the-loop (HIL), demonstrating its robustness and reliability in addressing challenges associated with internal disturbances, and FDIAs within the microgrid.

IEEE Transactions on Smart GridVol. 16(4)
King Fahd University of Petroleum and Minerals (SA), University of Electronic Science and Technology of China (CN), Aswan University (EG)
Climate action
Openalex Percentile: Top 12%
Smart Grid Security and Resilience
2.43
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