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Record W2106144599 · doi:10.1109/ccece.2011.6030603

Approximate dynamic average-value model for controlled line-commuted converters

2011· article· en· W2106144599 on OpenAlexaff
Hamid Atighechi, Sina Chiniforoosh, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConvertersComputer scienceTransient (computer programming)Parametric statisticsElectric power systemParametric modelLine (geometry)Frequency domainSmall-signal modelControl theory (sociology)Time domainPower (physics)Electronic engineeringBridge (graph theory)SIGNAL (programming language)VoltageEngineeringMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

Dynamic average-value modeling of power electronic-based systems has evolved and is widely used for system-level transient simulations and small-signal analysis. This paper presents an approximate average model for controlled line-commutated converters, which is developed based on the parametric average modeling methodology previously established for the diode bridge rectifiers. The proposed model is compared against a previously-developed analytical average-value model as well as a detailed switch- level implementation of the system. The new model is demonstrated to have good accuracy in both time- and frequency-domain for predicting the system-level behavior.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.952

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 integrity0.0000.000
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 head0.032
GPT teacher head0.233
Teacher spread0.201 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

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