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Record W1992098434 · doi:10.1109/pesgm.2012.6345513

Dynamic average modeling of front-end diode rectifier loads considering discontinuous conduction mode and unbalanced operation

2012· article· en· W1992098434 on OpenAlexaff
Sina Chiniforoosh, Hamid Atighechi, Ali Davoudi, Juri Jatskevich, Amirnaser Yazdani, Shaahin Filizadeh, M. Saeedifard, J.A. Martinez Velasco, Vijay K. Sood, Venkata Dinavahi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of AlbertaUniversity of ManitobaToronto Metropolitan UniversityUniversity of British Columbia
Fundersnot available
KeywordsRectifier (neural networks)Transient (computer programming)Computer scienceMATLABElectronic engineeringControl theory (sociology)Front and back endsPower (physics)Three-phaseVoltageEngineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

Summary form only given. Electric power distribution systems of many commercial and industrial sites often employ variable frequency drives and other loads that internally utilize dc. Such loads are often based on front-end line-commutated rectifiers. The detailed switch-level models of such rectifier systems can be readily implemented using a number of widely available digital programs and transient simulation tools including the EMT- (Electro-Magnetic Transient) and Matlab/Simulink. To improve the simulation efficiency for the system-level transient studies with large number of such subsystems, the so-called dynamic average models have been utilized. This paper presents the average-value modeling methodologies for the conventional three-phase (six-pulse) front-end rectifier loads. The paper demonstrates the system operation and the dynamic performance of the developed average models in discontinuous and continuous modes, as well as under balanced and unbalanced operation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.527

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2012
Admission routes1
Has abstractyes

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