A Prediction Interval Based Cascading Failure Prediction Model for Power Systems
Bibliographic record
Abstract
Power system blackouts result in massive supply interruptions leading to significant financial and societal losses. Since the majority of blackouts begin as a cascading failure (CF), early detection of this event can help stop the propagation of a single incident into a large-scale blackout. In this paper, a realtime load-based model for CF prediction is proposed. The developed method feeds phasor measurement units (PMU) data into a prediction interval (PI) neural network (NN) model reinforced with a data-fusion-based self-correction algorithm. The main contribution of the paper is that the model provides load shedding locations and prediction intervals regarding the expected blackout size so that the operator, or the automatic controller, can better react to the CF situation. The simulation results indicate that the proposed method is fast and accurate in predicting the size of the resulting blackout or load shedding following a CF.
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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".