Framework for a Real-Time Autonomous Cascading Failure Prediction Model
Bibliographic record
Abstract
Blackouts cause significant damage to both consumers and utilities. Since blackouts typically start as a cascading failure, the prediction of such a cascade can effectively prevent blackouts from propagating. The majority of the current cascading failure prediction models assume that the model only needs to be trained once, either when it is designed or when the system undergoes topology changes. However, this limits the efficacy and robustness of such models. Hence, this paper aims to design a framework for autonomous cascading failure prediction models that can self-improve while being connected to the grid in real-time. To successfully achieve this, importance sampling and case-based reasoning are used to optimize the amount of data and time needed to retrain the model in real-time. The results indicate that such an approach allows the models to naturally shift to a different model as the inputs change and significantly improves the accuracy of the model as more datapoints are obtained.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".