Vulnerability assessment and defence strategy to site distributed generation in smart grid
Bibliographic record
Abstract
Abstract False data injection (FDI) attacks tamper with the state estimation data and can pose significant threats to the smart grid. The vulnerability analysis and defence strategies may help to mitigate the impact of these attacks. However, existing research efforts have not addressed the computational power and accuracy issues in the vulnerability analysis and defence mechanisms using realistic test environments. In this work, the authors present a novel low‐complexity FDI attacks model to perform the vulnerability analysis. The authors develop a reduced‐row‐echelon‐form‐based greedy algorithm using the non‐linear power flow system to generate FDI attacks more accurately. Later, the authors propose a novel optimal defence strategy by developing a greedy algorithm. The authors' algorithm finds the optimal power assets' locations and defends against hidden FDI attacks with low computation cost. Finally, the authors utilize the proposed AC‐based attack and defence models to identify secure sites for distributed generation (DG) in the smart grid. The authors' experimental results for various IEEE standard test systems show enhanced accuracy of the attack and defence algorithms. The authors also validate the effectiveness of the proposed approaches in finding secure sites for DG units in the smart grid.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".