Optimization of high voltage substations using a random walk technique
Bibliographic record
Abstract
A grounding system is on one of the most important points inside transmission and power distribution systems. Poor design methods and simplified calculations can lead to high construction costs and unsafe conditions. This paper introduces a method to design a grounding grid while minimizing time and cost of construction. In this work, computer software has been developed using the equations to solve the optimization problem that considers the parameters of a grounding grid, including geometry, depth, conductor size and the number of grounding rods. The problem is formulated as a mixed integer linear optimization problem. The method incorporates the variables that define the grid characteristics of all possible configurations, including the grid geometry, grid depth, conductor size, and number of grounding rods, size of grounding rods, and, lastly, excavation and installation costs. The optimization problem is subject to safety constraints related to the maximum allowed ground potential rise (GPR), touch and step voltages. The method determines the optimum grid from several possible configurations, so that is a very useful tool for engineering design. A novel optimization technique using a random walk technique to find an optimized grounding grid in a two-layer soil model is proposed. Several examples prove the efficacy of the method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".