Medium-Voltage Current-Source Converter Drives for Marine Propulsion System Using a Dual-Winding Synchronous Machine
Bibliographic record
Abstract
Medium-voltage (MV) drives are increasingly used in high-power marine applications for running thrusters and main propulsion motors. In this paper, an MV drive solution employing active front-end current-source converters is proposed for a synchronous-motor-based propulsion system. The proposed solution includes two independent drives, each to control one of the two sets of three-phase windings of the synchronous motor. A dedicated communication link between the drives allows continuous load sharing and robust system operation in a master-follower drive configuration. Field-oriented control with the use of an absolute encoder is implemented for providing high starting torque and smooth speed control over a wide speed range including a 30% overspeed region. The major advantages of the proposed solution include simple structure, increased system power rating, redundant operation, low-harmonic input/output waveforms, improved reliability, elimination of a bulky input isolation transformer, and parallel drive control without the need of a complex coordinated inverter gating system. In addition, the system offers input power factor compensation and dynamic braking to allow operation on a generator-based supply system. Field test results obtained on a 14-MW ship propulsion system are provided to demonstrate the system performance.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".