Vulnerability assessment and defence strategy to site distributed generation in smart grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it