Graph Deviation Network With Physics-Informed Detection and Robust $H_{\infty }$ Control for Cyberattack Resilience in Wind-Integrated Power Grids
Bibliographic record
Abstract
Wind power plants (WPPs) rely significantly on extensive communication networks and data exchange for their operation and control. Thus, various cybersecurity issues can be created by their rapid integration into modern power grids. On this basis, this paper introduces novel denial-of-service (DoS), false data injection (FDI), and hybrid cyberattack models targeting rotor speed sensors of doubly-fed induction generator (DFIG)-based WPPs. The attacks are designed so that they excite lightly-damped oscillatory modes of the connected power grid while originating from a set of sensors in the WPP's turbines. Then, to counter the developed attacks, a graph deviation network (GDN) integrated with a physics-informed neural network (PINN) is developed for real-time cyberattack detection in a realistic noisy environment while maintaining compatibility with IEC-61400-25. Finally, a well-tailored robust$H_{\infty }$-based controller is designed to mitigate the impact of the sophisticated attacks and stabilize the power grid. The impact of the cyberattacks and the effectiveness of the proposed detection and mitigation framework are demonstrated on a modified New England 39-bus system, including practical deployment considerations and robustness under extended attack scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".