Power-Quality Impact Assessment for High-Speed Railway Associated With High-Speed Trains Using Train Timetable—Part II: Verifications, Estimations and Applications
Bibliographic record
Abstract
This paper investigates the voltage profiles and harmonic impacts of high-speed trains on the traction power-supply system (TPSS) and primary utility system. Power-quality (PQ) assessment has become not only a computing tool for TPSS design and planning, but also an indispensable technique for utilities to estimate the accurate PQ impacts from the railway systems. Therefore, to achieve comprehensive PQ assessment in TPSS, a dynamic fundamental/harmonic power-flow (DF/HPF) method is developed in a companion paper, while further application of the technique is described in this paper. The fundamental and harmonic results calculated in a 24-h period, such as loading levels, voltage profiles, unbalance, power loss, and harmonic distortions have been computed. In addition, the statistical measured background harmonics of the utility system are represented by Weibull function and considered in the harmonic evaluation. The unbalance and harmonic impacts are investigated and checked with national standards in this paper. The proposed method can be effective for excavating and predicting the potential serious PQ problems existing in the TPSS by using a train timetable.
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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 | 0.001 |
| 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 | 0.000 | 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".