Physics-Informed Neural Network-Based Detection of False Data Injection Attacks on FACTS-Assisted Wide-Area Fractional-Order Damping Controllers
- 1Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
- 2Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark
Abstract
False data injection attacks (FDIAs) pose a serious threat to FACTS-assisted wide-area damping controllers because they corrupt remote measurements, degrade inter-area oscillation damping, and may evade conventional detection. Data-driven detectors that ignore closed-loop physics are particularly vulnerable under multiplicative gain-ramp attacks; the problem is further exacerbated by the memory-dependent nature of fractional-order controllers, whose long-term history effects can mask attack-induced inconsistencies in the residuals unless system dynamics, actuator behavior, attack-channel constraints, and fractional-order memory are jointly embedded in the learning objective. This paper addresses that gap through a physics-informed neural network (PINN) framework that jointly estimates latent system states and reconstructs the multiplicative attack gain by incorporating plant dynamics, SVC actuator dynamics, measurement consistency, attack-channel reconstruction, and the reconstructed fractional-order command inside the SVC actuator residual. Detection is declared when a smoothed composite residual exceeds a calibrated threshold. In addition, a supervisory switching mechanism is introduced to replace corrupted wide-area control signals with reconstructed secure equivalents after confirmed detection, thereby restoring closed-loop damping performance without interrupting controller operation. Simulation results on a FACTS-based damping control system under gain-based FDIA scenarios show that the proposed detector achieves up to 99.42% accuracy, 98.86% precision, 99.18% recall, and a 99.02% F1-score, with a false alarm rate as low as 0.53%. The recovery mechanism also substantially reduces post-attack speed-deviation overshoot relative to the unmitigated case.
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BibTeX
@article{khaleghi2026physicsinformed,
title = {{Physics-Informed Neural Network-Based Detection of False Data Injection Attacks on FACTS-Assisted Wide-Area Fractional-Order Damping Controllers}},
author = {Ali Khaleghi and Soroush Oshnoei},
journal = {Fractal and Fractional},
year = {2026},
month = oct,
doi = {10.3390/fractalfract10100715},
url = {https://www.mdpi.com/2504-3110/10/10/715},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Physics-Informed Neural Network-Based Detection of False Data Injection Attacks on FACTS-Assisted Wide-Area Fractional-Order Damping Controllers
AU - Ali Khaleghi
AU - Soroush Oshnoei
PY - 2026
DA - 2026/10/11
JO - Fractal and Fractional
DO - 10.3390/fractalfract10100715
UR - https://www.mdpi.com/2504-3110/10/10/715
AB - False data injection attacks (FDIAs) pose a serious threat to FACTS-assisted wide-area damping controllers because they corrupt remote measurements, degrade inter-area oscillation damping, and may evade conventional detection. Data-driven detectors that ignore closed-loop physics are particularly vulnerable under multiplicative gain-ramp attacks; the problem is further exacerbated by the memory-dependent nature of fractional-order controllers, whose long-term history effects can mask attack-induced inconsistencies in the residuals unless system dynamics, actuator behavior, attack-channel constraints, and fractional-order memory are jointly embedded in the learning objective. This paper addresses that gap through a physics-informed neural network (PINN) framework that jointly estimates latent system states and reconstructs the multiplicative attack gain by incorporating plant dynamics, SVC actuator dynamics, measurement consistency, attack-channel reconstruction, and the reconstructed fractional-order command inside the SVC actuator residual. Detection is declared when a smoothed composite residual exceeds a calibrated threshold. In addition, a supervisory switching mechanism is introduced to replace corrupted wide-area control signals with reconstructed secure equivalents after confirmed detection, thereby restoring closed-loop damping performance without interrupting controller operation. Simulation results on a FACTS-based damping control system under gain-based FDIA scenarios show that the proposed detector achieves up to 99.42% accuracy, 98.86% precision, 99.18% recall, and a 99.02% F1-score, with a false alarm rate as low as 0.53%. The recovery mechanism also substantially reduces post-attack speed-deviation overshoot relative to the unmitigated case.
SN - 2504-3110
ER -