A Novel Approach to Precisely Calculate Lumped Parameters for Transmission Lines with Sag Using the M-Model Equivalent Circuit
Bibliographic record
Abstract
Real transmission lines are exposed to dynamic weather conditions and operated under different system states. These changes force power cables to sag, and thus the values of their distributed series and shunt parameters vary as well. This study is an attempt to account for the variations in these parameters by using a new highly precise medium-length transmission line model called the M-model. This lumped circuit represents the changes in the shunt parameters by a variable slack admittance placed in the middle of the circuit and the changes in the series parameters by two variable series impedances. Two approaches are proposed in this paper to realize the lumped shunt admittance. The first one divides the line into three ideal parts and then calculates the shunt admittance of the middle part at an equivalent height. The second one takes the ratio of the area below the line before and after sag. These methods can directly solve the inherent weaknesses associated with temperature-dependent studies without the necessity to know any temperature coefficient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".