Extended Moving Target Defense for AC State Estimation in Smart Grids
Bibliographic record
Abstract
The moving target defense (MTD) that proactively changes series reactance of transmission lines has recently been proposed as an effective defense approach to resist false data injection attacks in smart grids. However, the defense effectiveness analyses of MTD in existing research are mainly focused on linear DC state estimation. To bring the state-of-the-art research to practice, MTD for AC state estimation is investigated in this paper. Specifically, based on a thorough analysis, an extended MTD (EMTD) approach that coordinately changes series reactance and parallel susceptance of lines in smart grids is proposed to improve the traditional MTD. Moreover, the impact of EMTD on electricity market is analyzed. On this basis, the variation of locational marginal price, the variation of active power loss and the cost of devices for executing EMTD are treated as the cost of system defense. Furthermore, to find the trade-off between the defense effectiveness and the cost of EMTD, optimal construction of cost-minimization EMTD topology parameter scheme and defense time interval are also proposed. Finally, extensive simulations are conducted on the standard IEEE test system to demonstrate the effectiveness of the proposed approach.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".