A Cyber-Physical Control Framework for Transient Stability in Smart Grids
Bibliographic record
Abstract
Denial of service attacks and communication latency pose challenges for the operation of control systems within power systems. Specifically, excessive delay between sensors and controllers can substantially worsen the performance of distributed control schemes. In this paper, we propose a framework for delay-resilient cyber-physical control of smart grid systems for transient stability applications. The proposed control scheme adapts its structure depending on the value of the latency. As an example, we consider a parametric feedback linearization (PFL) control paradigm and make it “cyber-aware.” A delay-adaptive design that capitalizes on the features of PFL control is presented to enhance the time-delay tolerance of the power system. Depending on the information latency present in the smart grid, the parameters and the structure of the PFL controller are adapted accordingly to optimize performance. The improved resilience is demonstrated by applying the PFL controller to the New England 39-bus and WECC 9-bus test power systems following the occurrence of physical and cyber disturbances. Numerical results show that the proposed cyber-physical controller can tolerate substantial delays without noticeable performance degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".