AC Electromagnetic Interference Study between Railways and Nearby Power Lines under Steady-State Operation
Bibliographic record
Abstract
AC electromagnetic interference (EMI) between railways and nearby power lines can compromise reliable operations of railway signal and protection systems and increase electrical shock hazards to the public and railway personnel. To maintain the continuity of railway operation and meet the railway safety standard, an AC EMI study is crucial. However, modeling AC EMI patterns in railways due to nearby power lines is complex, involving parameterization and characterization of AC EMI, and modeling the overall railway-power line system. Variations of design parameters along the right-of-way of rail tracks should also be considered. In this paper, the steps to conduct an AC EMI study for a rail system in the vicinity of a power line using the CDEGS software package is proposed. Sensitivity studies are conducted considering variations of several parameters, including the separation distance between railways and nearby power lines, length of the railway, ballast resistance, soil resistivity, power line loading, and voltage class of power lines. Based on results of these studies, a general guideline is developed in this paper to recommend the minimum separation distances between railways and power lines to mitigate AC EMI. An initial AC EMI assessment for a particular railway-power line system can be conducted using this guideline to determine if there are safety concerns and a detailed and costly engineering study is required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".