Strategy to Reduce the Electric Field in Transmission Lines, Modifying the Geometry of the Tower and Its Bundle Configuration
Bibliographic record
Abstract
This article presents a methodology designed to reduce the electric field in both single and double circuit overhead transmission lines.To achieve this objective, the computational tool called EMFC-DoubleCTL is used, which was developed in the MATLAB App Designer environment by the CEM Research Group of the Francisco José de Caldas District University.This tool has a friendly and intuitive interface that allows obtaining results with acceptable accuracy and low computational time compared to specialized software in the electromagnetic area.EMFC-DoubleCTL allows adjust parameters associated with the configuration of the three-phase system, the geometry of the line and the bundle of conductors, to subsequently obtain the results of the capacitance matrix, the magnitude of the field in effective value, horizontal and vertical component variant in time and field ellipse.One of the most notable characteristics of the tool is that in many cases, obtaining the results associated with the electric and magnetic field in transmission lines turns out to be tedious, since simulators such as COMSOL require separate calculations and parameterizations for the time and frequency domain.On the other hand, EMFC-DoubleCTL is capable of providing these graphs in a single module, which simplifies the parameterization of the line under study.This methodology is designed to be applied to a case study in which the original geometry of the line will be used and changes will be made both in its geometry and in the bundle configuration of the conductors, in order to evaluate whether these changes allow reduce the magnitude of the electric field in the bonded area expressed in effective value with respect to the original configuration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".