Method to include lumped devices in multi-conductor transmission line system models
Bibliographic record
Abstract
Transient analysis and crosstalk in multiconductor transmission line (MTL) systems with internal and external losses has been widely studied by solving telegrapher's equation using the finite difference time domain (FDTD) method. Most of those studies are carried out with lumped loads/devices connected at the line terminations (at source and load ends). However, in practical systems such as typical railway and power systems, lumped devices, like, transformers, rotary converter stations, line interconnections and insulators, substation and switchgear equipments (grounding systems, insulators, surge protectors, circuit breakers, etc.) and signaling equipments exist either in series or in shunt with the MTL systems, but not necessarily at the ends. Detailed/complex circuit models (linear or nonlinear) for all those devices either exist in the literature or can be developed from experiments. In this paper a more general method based on Kirchoff's current law (nodal analysis) is proposed to interface the FDTD code with Alternative Transients Program/Electromagnetic Transients Program (ATP/EMTP) circuit simulation software for transient analysis with any complex circuit model along the MTL system. The method is efficient and simple and it could be beneficial for accurate and realistic transient protection, mitigation and insulation coordination studies in large distributed electrical systems. A case study is made for an electrified railway with booster transformer traction system.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".