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Record W2318633109 · doi:10.1109/tia.2014.2316361

Medium-Voltage Current-Source Converter Drives for Marine Propulsion System Using a Dual-Winding Synchronous Machine

2014· article· en· W2318633109 on OpenAlexaff
Jingya Dai, Sang Woo Nam, Manish Pande, Ghodrat Esmaeili

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2014
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsRockwell Automation (Canada)
Fundersnot available
KeywordsPropulsionSynchronous motorConvertersEngineeringControl theory (sociology)TorqueControl systemComputer scienceVoltageElectrical engineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.243
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations47
Published2014
Admission routes1
Has abstractyes

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