Analysis of Data-Driven Modeling of Cycloconverters for Efficient Electromagnetic Transient Simulations of Electrified Ship Propulsion Systems
Bibliographic record
Abstract
Simulation studies of modern electrified ship propulsion systems using the discrete switching models of cycloconverters are very time-consuming and require expertise and accuracy in modeling all the details of the ship’s electric power system. Alternatively, data-driven models of cycloconverter-based Variable-Frequency-Drive (VFD) systems are proposed, which may simplify the modeling and improve simulation efficiency and speed. The data-driven models can be established based on several measurements or a few runs of the detailed simulations, but their subsequent use enables very fast and efficient systemlevel studies. In this paper, the data-driven models of cycloconverter-based VFDs are analyzed in terms of their accuracy and numerical efficiency with respect to their detailed switching model counterparts for harmonic studies of an example ship propulsion system. The advantages and drawbacks of both modeling techniques are demonstrated through time-domain and frequency-domain electromagnetic transient simulations conducted in MATLAB/Simulink using the Simscape Electrical toolbox.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".