Predicting power system reliability and outage duration including emergency response
Bibliographic record
Abstract
Aim. Enable prediction and planning for large-scale unprecedented power outages of importance for emergency planning and national response actions. Predict outage probability, duration and restoration using a theoretical framework that is applicable globally. Methods. Data have been collected for power losses and outage duration for a wide range of events in Belgium, Canada, Eire, France, Japan, Sweden, New Zealand and USA. A new theory and correlation is given for the probability of large regional power losses of up to nearly 50,000 MW(e) without additional infrastructure or grid damage. For severe and rare events with damage (major floods, fire, ice storms, hurricanes etc.) the outages are longer and the restoration probability depends on the degree of difficulty that limits access and restoration. The dynamic reliability requirements for emergency back-up power and pumping systems are derived, and demonstrated using the flooding of New Orleans by Hurricane Katrina and of the Fukushima nuclear reactors by a tsunami. Conclusions . Explicit expressions have been given and validated for the probability and duration for the full range from “normal” large power losses to extended outages due to rare and more severe events with access and repair difficulty.
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.
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 | 0.001 | 0.001 |
| 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".