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Enhanced Average-Value Modeling of Voltage-Source Inverters in Variable Frequency Drives for Efficient Simulation of Marine Propulsion Systems

2023· article· en· W4392943775 on OpenAlexaff
Seyyedmilad Ebrahimi, Taleb Vahabzadeh, Arash Safavizadeh, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Data Processing Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPropulsionVariable (mathematics)Marine propulsionVoltageValue (mathematics)Switching frequencyComputer scienceControl theory (sociology)Electrical engineeringEngineeringAerospace engineeringMathematicsControl (management)

Abstract

fetched live from OpenAlex

Variable-frequency drive (VFD) systems utilize voltage-source inverters (VSIs) and are employed extensively in many applications, including marine propulsion. For efficient simulations and studies of such systems, average-value models (AVMs) of VSIs are indispensable. Recently, a so-called directly-interfaced AVM (DI-AVM) has been developed for line-commutated and voltage-source converters. In this paper, the DIAVM is developed for VSIs in the converter reference frame, which has advantages for machine-converter systems. The DI-AVM is implemented in electromagnetic transient (EMT) simulation programs as a resistance/conductance matrix instead of controlled voltage/current sources that are used conventionally. The advantages of the proposed DI-AVM are demonstrated on a ship propulsion system implemented in PSCAD/EMTDC. The proposed DI-AVM offers significant computational advantages compared to the conventional AVMs of VSIs by achieving higher numerical accuracy and allowing larger time step sizes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations6
Published2023
Admission routes1
Has abstractyes

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