Dynamic-Line-Rating-Based Robust Corrective Dispatch Against Load Redistribution Attacks With Unknown Objectives
Bibliographic record
Abstract
Load redistribution (LR) attacks have proven to be hard-detectable and damaging, which require effective corrective schemes to mitigate the impact on power grid operations. Traditional game-theoretic methods and corrective dispatches employing static line rating (SLR) have been studied for attack mitigation based on specific attack objectives but have high dispatch cost and limited performance of attack mitigation. This is because the power transfer capacity of the existing transmission network is underestimated with SLR, and in practical operations, the specific objective of the adversary is not available to the defender, which would introduce uncertainties to the design of corrective schemes. As such, this article incorporates the dynamic line rating (DLR) technology, which enhances the power transfer capability of the existing network, to develop the cost-effective corrective dispatch for mitigating LR attacks with unknown objectives. Specifically, a DLR-based robust corrective (DRC) dispatch model is presented, which guarantees the system security as well as the economic performance. A methodology utilizing the robust counterpart technique and column constraint generation (CCG) algorithm is proposed to solve the dispatch model in a decomposition framework. Case studies based on the IEEE 14- and 118-bus systems verify the performance of the proposed DRC dispatch in enhancing the cyber–physical security of power grids.
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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 | 0.001 | 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".