Physics-Informed Neural Network-Based Detection of False Data Injection Attacks on FACTS-Assisted Wide-Area Fractional-Order Damping Controllers

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.