A Real-time Quantitative Framework for Survivability Evaluation of Smart Grids
Bibliographic record
Abstract
Responding to high impact events, such as cyberattacks, during the first phase of resilience, i.e., survive, plays an important role in a system resilience enhancement. To this end, a real-time quantitative framework is highly needed. Motivated by this, this paper proposes a novel real-time cyber-physical resilience-based survivability metric for smart grids’ survivability assessment. The proposed method takes into account the survivability of both the electrical, i.e., physical, and cyber systems. A survivability margin is established for Power-side Survivability (PsS) to monitor the power system’s capacity to maintain the functionality of its critical components. Available Generation Capacity (AGC) and Network Accordant Connectivity (NAC) are factors that are considered while calculating PsS. Based on the available pathways between the Human-Machine Interface (HMI) and Intelligent Electronic Devices (IEDs) in a substation, the Cyber-side Survivability (CsS) is calculated for a given cyber configuration. The computational complexity of the metric calculation limits the metric to real-time measurement. To address this issue, the Histogram-based Gradient Boosting Regression Tree (HGBRT) model is used to forecast the metric depending on system circumstances. To demonstrate its efficacy, the proposed metric is tested on the PJM 5-bus system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".