Reliability assessment of transmission and distribution systems considering repair in adverse weather conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The physical environment in which a transmission and distribution system resides has a significant impact on the resulting reliability of the network. Many researchers throughout the world believe that extreme adverse weather is becoming more frequent and severe. This paper illustrates a model that can be utilized for the predictive assessment of reliability indices in both adverse and extreme adverse weather conditions. Incorporating the failures that take place in extreme adverse weather calls for different models and techniques. In this paper, analysis is done using three different weather models: the conventional single and two weather state models and a three weather state model. The model presented in the paper incorporates the ability to consider repair in adverse weather and examines the effect of including this in the analysis. The ability to conduct repair during adverse weather conditions has a positive effect on the reliability indices of average system failure rate and average system outage duration.
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.
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 it