A reliability improvement roadmap based on a predictive model and extrapolation technique
Bibliographic record
Abstract
This paper explains the development of a ten-year reliability improvement roadmap for a major distribution utility of the USA. First, a benchmark approach based on a survey of the reliability indices of 21 utilities of the USA and Canada was used to set the roadmap targets. Moreover, a historical outage analysis was performed to identify the main outage causes and potential reliability improvement options. Then, a detailed predictive reliability model was used to assess the cost-effectiveness of a broad set of reliability improvement projects for a pilot study area. Finally, the results of the study area were extrapolated to the utility distribution system by using a novel technique. Here, in order to consider the differences between the study area and the utility distribution system (representativeness error), the main characteristics of each feeder (length, number of customers per circuit mile, percentage of overhead and underground exposure, voltage level, etc) were taken into account. The reliability roadmap results for the utility system are presented and discussed.
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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".