A DoS-resilient Set-Theoretic Controller for Smart Grid Applications
Bibliographic record
Abstract
In this paper, we address the problem of designing a Denial of Service (DoS)-resilient controller for robustly recovering transient stability in smart grids. In particular, we consider a scenario where, during the recovery phase, a finite-time DoS attack affects the measurement channel. We propose a Model Predictive Control (MPC) controller based on the set-theoretic arguments, which is capable of dealing with both model uncertainties, actuator limitations and DoS. Unlike traditional robust MPC solutions, the proposed controller is particularly appealing for smart grid applications given its intrinsic capability of moving most of the required computations into an offline phase. The online phase requires the solution of a quadratic programming problem, which can be efficiently solved in real-time. Moreover, the proposed controller ensures a worst-case time to recovery irrespective of any admissible perturbation and DoS realization. Finally, by considering the New England 10-generator 39-bus system, simulation results are presented to show the advantage of the proposed solution when contrasted with another recently proposed scheme.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".