The Impact of Cybersecurity on Siting Distributed Generation Units in AC Power Systems
Bibliographic record
Abstract
The vulnerabilities of the smart grid to cyberattacks can pose dangerous threats to the assets connected to the power network. It is important to identify cyber vulnerabilities in power systems and implement effective defense mechanisms to prevent cyber threats on these assets. Existing research works have not shown effective techniques for the vulnerability assessment and countermeasures against cyberattacks for the AC power systems. In this paper, we emphasize the impact of cybersecurity on siting distributed generation (DG) units in the AC power grid. We present an AC-based false data injection (FDI) attack model to analyze the cyber vulnerabilities of the system and propose an optimal defense strategy to prevent cyber incidents on these units. We perform experiments on the SaskPower network of Saskatchewan, Canada, to demonstrate the optimal siting for DG units under the cybersecurity constraint. Our simulation results obtained for the SaskPower grid show the effectiveness of our proposed approaches to analyze the cyber vulnerabilities of the power grid and find secure sites for the DG units in Saskatchewan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".