Ferramenta Computacional para o Planejamento da Expansão de Redes de Distribuição Considerando Confiabilidade
Bibliographic record
Abstract
This work proposes a software tool to help plan the expansion of the primary distribution network of power distribution companies, incorporating reliability criteria. The software is based on a method that we developed to analytically assess the reliability using the information of feeders and historical data regarding occurrences of interruptions. According to this method, the data available concerning distribution systems are used to estimate reliability indices with and without expanding the network; further, the user can choose the best expansion action and assess the associated impacts as well. To demonstrate the aid in the process of decision-making for expansion planning, the software was applied to a network composed of eight distribution feeders connected to a substation. The installation and automation of normally-closed sectionalizing switches were analyzed; nevertheless, the software is flexible enough so that different types of expansion alternatives can be easily integrated. The results demonstrated that the tool is able to guide the location of installation and automation of sectionalizing switches by identifying the zones in which faults most contribute to the reliability indices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".