Distributed Resilient Secondary Control of Smart Grid Under FDI Attacks
Bibliographic record
Abstract
This paper addresses the critical challenge of False Data Injection Attacks (FDIAs) targeting both sensors and controllers in islanded smart grids, which threaten the stability of frequency and voltage regulation in distributed secondary control systems. Conventional distributed control strategies are shown to be vulnerable under coordinated FDIAs, leading to desynchronization and control objective failures. To overcome these limitations, a novel distributed resilient secondary control strategy is proposed, integrating attack mitigation directly into the control framework without relying on explicit detection mechanisms. The core contributions include: (1) A comprehensive vulnerability analysis of smart grid secondary control systems under simultaneous sensor-controller FDIAs, supported by Lyapunov-based stability proofs; (2) The development of resilient frequency and voltage controllers capable of compensating attack-induced errors through adaptive estimation and distributed consensus; (3) Rigorous theoretical guarantees for global asymptotic stability under bounded attacks, with convergence rates quantified through eigenvalue analysis of the enhanced Laplacian matrix$L$+$G$. Extensive validation via MATLAB/Simulink simulations demonstrates smart grids system under multiple attack scenarios. This work provides a paradigm shift in smart grid cybersecurity, establishing a detection-free resilient control framework that ensures operational continuity even during sustained cyber attacks.
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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) | 0.001 | 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 | 0.000 | 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".