DC Component From Pantograph Arcing in AC Traction System—Influencing Parameters, Impact, and Mitigation Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it