Power-Quality Impact Assessment for High-Speed Railway Associated With High-Speed Trains Using Train Timetable—Part I: Methodology and Modeling
Bibliographic record
Abstract
The proliferation of the voltage-source converter-based high-speed trains has resulted in significant distortions in voltage and current waveforms in both traction power supply system and the utility system. The dynamic behaviors of the high-speed trains (HSTs) make the assessment of such power-quality (PQ) problems quite difficult. There is an urgent need for techniques that can quantify the collective PQ impacts of modern trains during a 24-h period. Dynamic behavior modeling of the modern trains during the operating duty period between two station stops is studied here for PQ assessment. The 24-h profiles of the train timetable and rail infrastructure are entered to compute the information, including train positions, speeds, power consumptions, etc. Moreover, six sets of the measurement-based Norton-equivalent model under different operations are implemented to represent the dynamic harmonic behaviors of the train. In addition, the systemic modeling of the utility system, traction lines, and Scott-connection transformer is also described. After comparing the results of calculations and measurements, the proposed model is ideally effective for analyzing the consequences of HST's dynamic behavior and system topology that are involved in fundamental power flow and harmonic power flow in order to evaluate the comprehensive PQ impacts in a companion paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".