DC Component From Pantograph Arcing in AC Traction System—Influencing Parameters, Impact, and Mitigation Techniques
Bibliographic record
Abstract
Pantograph arcing in ac traction system generates transients, and causes asymmetries and distortion in supply voltage and current waveforms. These asymmetric voltage and current lead to a net dc component and harmonics that propagate within the traction power and signalling system and cause electromagnetic interference. This problem is enhanced during winter because of the layer of ice/snow on the overhead contact wire. The sliding contact becomes poor and a visible arc moves along with the pantograph. In this paper, it is shown how different parameters like traction current, line speed, power factor, and supply voltage influence the arcing, its characteristics, and the dc components. It is shown that the dc current component increases with increasing train speed and traction current, and reduces at a lower power factor. It is also discussed how the presence of an ice layer influences the arcing and the dc components. It is found that running the trains below the normal operating power factors is an effective choice to mitigate this problem. The findings presented in this paper could be beneficial to estimate the probable limit of the dc component at the planning stage so that proper precautions can be taken at the design stage itself.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".