A Multi-layer Resilient Control Framework for Networked Microgrids against False Data Injection Attacks

HUANG yu; CAO zhengyang; HU songlin; YUE dong; CHEN yonghua; YAN yunsong

doi:10.11999/JEIT250850

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HUANG yu, CAO zhengyang, HU songlin, YUE dong, CHEN yonghua, YAN yunsong. A Multi-layer Resilient Control Framework for Networked Microgrids against False Data Injection Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250850

Citation:

HUANG yu, CAO zhengyang, HU songlin, YUE dong, CHEN yonghua, YAN yunsong. A Multi-layer Resilient Control Framework for Networked Microgrids against False Data Injection Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250850

Citation:

HUANG yu, CAO zhengyang, HU songlin, YUE dong, CHEN yonghua, YAN yunsong. A Multi-layer Resilient Control Framework for Networked Microgrids against False Data Injection Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250850

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A Multi-layer Resilient Control Framework for Networked Microgrids against False Data Injection Attacks

doi: 10.11999/JEIT250850 cstr: 32379.14.JEIT250850

1.
Institute of Advanced Technology for Carbon Neutrality, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2.
NARI Technology Co., Ltd, Nanjing 211106, China

Funds: The Natural Science Foundation of Jiangsu Province (BK20232026)

Received Date: 2025-09-01
Accepted Date: 2026-04-08
Rev Recd Date: 2026-04-07

Available Online: 2026-04-25

Abstract

Abstract

Objective With the increasing penetration of distributed renewable energy and the growing dependence on cyber-physical infrastructure, Networked MicroGrids (NMGs) are increasingly vulnerable to False Data Injection Attacks (FDIAs). These attacks threaten frequency stability and system security. Traditional secondary control methods are limited by constrained communication resources and fixed sampling mechanisms. They often fail to maintain resilient operation under stealthy FDIAs and dynamic disturbances. To address these challenges, this study develops a multi-layer resilient control strategy that integrates event-triggered communication/control, data-driven attack observation, and double-replay Q-learning. The objective is to improve communication efficiency, attack detection, and stability recovery in NMGs under complex cyber threats. Methods The proposed Event-Triggered Control–Radial Basis Function–Double-Replay Q-Learning (ETC–RBF–DRQL) framework integrates an Event-Triggered Control (ETC) mechanism, a Radial Basis Function Unknown Input Observer (RBF-UIO), and a Double-Replay Q-Learning (DRQL) compensator to achieve resilient frequency control in NMGs under FDIAs. The ETC mechanism reduces redundant data transmission while maintaining system stability. The RBF-UIO estimates system states and detects anomalous deviations. After an attack is detected, the DRQL module adaptively generates compensation signals to suppress the attack effect and restore system stability. The framework is formulated using a modular dynamic model of NMGs, which supports stability analysis under communication and attack constraints. Simulation experiments are conducted on a 4-node distributed microgrid testbed in MATLAB/Simulink. The testbed includes different renewable energy sources and realistic communication links to verify the effectiveness and scalability of the proposed approach. Results and Discussions The proposed ETC–RBF–DRQL framework is validated on a 4-node NMG under FDIA scenarios. Simulation results show that the method achieves better overall performance in frequency regulation, communication efficiency, and attack resilience. Specifically, the frequency deviation peak is reduced from 0.021 8 Hz under periodic Proportional-Integral (PI) control to 0.012 1 Hz. The steady-state average deviation and fluctuation standard deviation are reduced to 0.009 7 Hz and 0.007 4 Hz, respectively (Fig. 4, Table 2). The average communication event rate decreases to 11.9 pkt·s^-1, corresponding to a 76.2% reduction compared with periodic sampling (Table 2). The proposed framework also maintains reliable attack detection performance, with a detection rate of 91.5%, a false alarm rate of 4.8%, and an area under the curve (AUC) of 0.968 (Table 2). These results indicate that the proposed method can coordinate frequency recovery, communication overhead reduction, and FDIA mitigation in NMGs. Conclusions This paper investigates a multi-layer resilient control framework for NMGs under FDIAs and communication constraints. The proposed ETC–RBF–DRQL method integrates event-triggered communication/control, RBF-UIO-based attack detection, and DRQL-based adaptive compensation. It therefore enables closed-loop coordination among anomaly detection, attack suppression, and frequency stability recovery. Simulation results on a 4-node NMG show that, compared with conventional PI-based schemes, the proposed approach reduces frequency deviation peaks and shortens recovery time while lowering communication overhead. Theoretical analysis further confirms its feasibility and stability under bounded estimation errors. This study focuses on sensor-side FDIAs and simplified communication conditions. Future work will consider more complex multi-type attacks and hardware-in-the-loop validation to support engineering applications.

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