Resilient Event-Triggered Observer-Based Periodic Wide-Area Control for Oscillation Damping in WAMPAC Systems Under Time Synchronization Attacks
Bibliographic record
Abstract
In this article, we address the problem of delay-causing time synchronization (DC-TS) attacks against wide-area damping controllers (WADCs). To enhance smart grid stability against such threats, we present a realistic and secure design procedure for WADCs. To this end, we follow a methodology that utilizes the state-space model of the entire grid to design a periodic observer-based event-triggered controller by formulating the problem as a set of linear matrix inequalities, solved by the looped-Lyapunov functional (LLF) technique. The event-triggered scheme applied in this design procedure improves communication efficiency. Plus, the periodic sampled-data approach makes the design better suited to the operational reality of digital systems and their constraints. As such, the contributions of this work include developing an event-triggered mechanism to reduce unnecessary data transmissions, applying LLF for less conservative stability analysis, and utilizing the Guardian map theorem and Rekasius substitution to assess the WADCs resilience under DC-TS attacks. We conducted extensive simulations on the Kundur two-area and New England 39-bus systems to validate our approach. These simulations, along with comparisons to existing methods and tests on the RT-Lab real-time platform, demonstrate the superior performance of our WADC in maintaining grid stability and improving damping under considered 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".