DISTRIBUTIONS OF LARGE-SCALE POWER OUTAGES: EXTREME VALUES AND THE EFFECT OF TRUNCATION
Bibliographic record
Abstract
In this study, we examine the distribution of large-scale power outages using a very current database of outage events reported to the Department of Energy. Recent theoretical studies applying complex systems theory to the study of power outages have predicted that the magnitude of such events should follow a power law distribution of form 1/x α+1 over certain time scales. The high probability of large events under this distribution leads to serious risk management consequences. We directly analyze outage events over the period 1984-2006 and find that the behaviour of actual outage events is actually much closer to that of a log-normal distribution. Various distributions are used to fit the data using modern statistical methodologies. The statistical theory of extremes is introduced to take into account the truncation of small values due to reporting criteria. Risk management consequences of various tail behaviours of the outage distribution are also examined.
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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 | 0.001 | 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".