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Detection-Triggered Recursive Impact Mitigation against Secondary False Data Injection Attacks in Microgrids

Mengxiang Liu, Xin Zhang, Rui Zhang, Zhuoran Zhou, Zhenyong Zhang, Ruilong Deng

TL;DR

This paper addresses secondary false data injection attacks (SFDIAs) on microgrid communication by introducing a detection-triggered recursive bias reconstruction framework that leverages Unknown Input Observers (UIOs) residuals and physical interconnections via power-line currents. It derives explicit residual–bias relations, reconstructs current-bias injections using observed voltage biases, and mitigates attack impacts by correcting compromised data, even when all links are compromised. A cost-efficient current-sensor deployment strategy based on spanning-tree subgraphs reduces hardware needs while preserving secondary-control performance. The approach is validated through extensive MATLAB/Simulink simulations and hardware-in-the-loop experiments, showing bounded reconstruction errors, robust performance under load and voltage fluctuations, and real-time feasibility with lightweight computation.

Abstract

The cybersecurity of microgrid has received widespread attentions due to the frequently reported attack accidents against distributed energy resource (DER) manufactures. Numerous impact mitigation schemes have been proposed to reduce or eliminate the impacts of false data injection attacks (FDIAs). Nevertheless, the existing methods either requires at least one neighboring trustworthy agent or may bring in unacceptable cost burdens. This paper aims to propose a detection-triggered recursive impact mitigation scheme that can timely and precisely counter the secondary FDIAs (SFDIAs) against the communication links among DERs. Once triggering attack alarms, the power line current readings will be utilised to observe the voltage bias injections through the physical interconnections among DERs, based on which the current bias injections can be recursively reconstructed from the residuals generated by unknown input observers (UIOs). The attack impacts are eliminated by subtracting the reconstructed bias from the incoming compromised data. The proposed mitigation method can work even in the worst case where all communication links are under SFDIAs and only require extra current sensors. The bias reconstruction performance under initial errors and system noises is theoretically analysed and the reconstruction error is proved to be bounded regardless of the electrical parameters. To avoid deploying current sensors on all power lines, a cost-effective deployment strategy is presented to secure a spanning tree set of communication links that can guarantee the secondary control performance. Extensive validation studies are conducted in MATLAB/SIMULINK and hardware-in-the-loop (HIL) testbeds to validate the proposed method's effectiveness against single/multiple and continuous/discontinuous SFDIAs.

Detection-Triggered Recursive Impact Mitigation against Secondary False Data Injection Attacks in Microgrids

TL;DR

This paper addresses secondary false data injection attacks (SFDIAs) on microgrid communication by introducing a detection-triggered recursive bias reconstruction framework that leverages Unknown Input Observers (UIOs) residuals and physical interconnections via power-line currents. It derives explicit residual–bias relations, reconstructs current-bias injections using observed voltage biases, and mitigates attack impacts by correcting compromised data, even when all links are compromised. A cost-efficient current-sensor deployment strategy based on spanning-tree subgraphs reduces hardware needs while preserving secondary-control performance. The approach is validated through extensive MATLAB/Simulink simulations and hardware-in-the-loop experiments, showing bounded reconstruction errors, robust performance under load and voltage fluctuations, and real-time feasibility with lightweight computation.

Abstract

The cybersecurity of microgrid has received widespread attentions due to the frequently reported attack accidents against distributed energy resource (DER) manufactures. Numerous impact mitigation schemes have been proposed to reduce or eliminate the impacts of false data injection attacks (FDIAs). Nevertheless, the existing methods either requires at least one neighboring trustworthy agent or may bring in unacceptable cost burdens. This paper aims to propose a detection-triggered recursive impact mitigation scheme that can timely and precisely counter the secondary FDIAs (SFDIAs) against the communication links among DERs. Once triggering attack alarms, the power line current readings will be utilised to observe the voltage bias injections through the physical interconnections among DERs, based on which the current bias injections can be recursively reconstructed from the residuals generated by unknown input observers (UIOs). The attack impacts are eliminated by subtracting the reconstructed bias from the incoming compromised data. The proposed mitigation method can work even in the worst case where all communication links are under SFDIAs and only require extra current sensors. The bias reconstruction performance under initial errors and system noises is theoretically analysed and the reconstruction error is proved to be bounded regardless of the electrical parameters. To avoid deploying current sensors on all power lines, a cost-effective deployment strategy is presented to secure a spanning tree set of communication links that can guarantee the secondary control performance. Extensive validation studies are conducted in MATLAB/SIMULINK and hardware-in-the-loop (HIL) testbeds to validate the proposed method's effectiveness against single/multiple and continuous/discontinuous SFDIAs.
Paper Structure (28 sections, 2 theorems, 37 equations, 16 figures, 1 table, 2 algorithms)

This paper contains 28 sections, 2 theorems, 37 equations, 16 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Isolated Microgrid System Model
  3. Attack Model
  4. Unknown Input Observer based Attack Detection
  5. Motivating Example and Problem Formulation
  6. Detection-Triggered Bias Reconstruction and Impact Mitigation
  7. Explicit Relations between Residuals and SFDIA Biases
  8. Physical-Interconnection-Aware Bias Reconstruction
  9. Theoretical Performance Analysis
  10. Impact Mitigation
  11. Cost-Effective Current Sensor Deployment
  12. Estimation of Power Line Current Sum
  13. Cost-Effective Deployment Strategy of Current Sensors
  14. Simulation Studies and Experimental Validations
  15. Reconstruction Stability under Varying Parameters
  16. ...and 13 more sections

Key Result

Proposition 1

Under the bias reconstruction scheme eq: SFDIA bias reconstruction, when the bias observations are accurate as claimed in eq: Observed SFDIA voltage bias, the steady-state current bias reconstruction error, denoted by $\varphi_{i,j,I}^{sy}(k) = \phi_{i,j,I}^{sy}(k) - \phi_{i,j,I}^{sy,re}(k)$, will b if the eigenvalue of the reconstruction dynamics eq: SFDIA bias reconstruction, i.e., $\eta_j^s$, s

Figures (16)

  • Figure 1: This figure illustrates the locations of five typical FDIAs against primary and secondary controllers in the microgrid.
  • Figure 2: The left part of the figure depicts the highly abstracted cyber and physical framework of microgrids, the middle part illustrates the specific cyber-physical couplings and exposed attack surfaces to SFDIAs, and the right part shows the graphical framework of the detection-triggered recursive bias reconstruction and impact mitigation scheme and its integration with original secondary controllers.
  • Figure 3: This figure illustrates a practical attack path consisting of four key steps for launching the SFDIA in microgrids.
  • Figure 4: Motivating examples indicating the relations of UIOs' residuals and Step, Ramp, and Sine bias injections.
  • Figure 5: This figure elaborates the working flow of the detection-triggered bias reconstruction and impact mitigation scheme.
  • ...and 11 more figures

Theorems & Definitions (4)

  • Proposition 1
  • Proposition 2
  • Proof 1
  • Proof 2