Optimal Requirements of Spare Transformers and Mobile Units for Distribution Substations via Genetic Algorithm and Monte Carlo Techniques
Bibliographic record
Abstract
This paper proposes a new optimization method based on enhanced genetic algorithm (GA) and Monte Carlo simulation (MCS) techniques, which are simultaneously applied to size regular spare transformer (RST) and mobile unit substations (MUS) stocks for distribution substations. The aim is to serve a group of electrical energy distribution substations to mitigate possible losses caused by load curtailments due to major failures that affect the substation transformers. The proposed method includes the use of resources such as MUS and load transfer, in addition to representing the expansion of the transformers group in operation and the increase in power demand, over a specified planning horizon, considering all waiting times inherent to system actions, e.g.,: RST installation, MUS connection, stock replenishment, etc. Two real systems with different characteristics are used to illustrate the proposed method, allowing the analysis of results obtained from different scenarios and parameters.
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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".