{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada",
    "Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ali Khaleghi",
      "orcid": "0000-0002-6635-0674"
    },
    {
      "affs": [
        1
      ],
      "name": "Soroush Oshnoei",
      "orcid": "0000-0002-5059-3065"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9c7h7gd34bcg0.bib",
  "cited_by": 0,
  "doi": "10.3390/fractalfract10100715",
  "fulltext_url": null,
  "id": "9c7h7gd34bcg0",
  "issns": [
    "2504-3110"
  ],
  "landing_url": "https://www.mdpi.com/2504-3110/10/10/715",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2207|Smart Grid Security and Resilience"
    ]
  ],
  "openalex": "W7221507211",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/9c7h7gd34bcg0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:32Z",
      "native_id": "10.3390/fractalfract10100715",
      "source": "crossref",
      "url": "https://www.mdpi.com/2504-3110/10/10/715"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2504-3110",
      "subfield": "analysis-pdes"
    }
  ],
  "title": "Physics-Informed Neural Network-Based Detection of False Data Injection Attacks on FACTS-Assisted Wide-Area Fractional-Order Damping Controllers",
  "title_text": "Physics-Informed Neural Network-Based Detection of False Data Injection Attacks on FACTS-Assisted Wide-Area Fractional-Order Damping Controllers",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:32Z",
  "url": "https://research.draconitic.com/paper/9c7h7gd34bcg0",
  "venue": "Fractal and Fractional",
  "versions": []
}