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Record W2063856363 · doi:10.1109/itec.2012.6243440

Simulation-based dynamic characterization of transformer-isolated machine-rectifier systems

2012· article· en· W2063856363 on OpenAlexaff
Ali Davoudi, Hamid Behjati, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTransfer functionLinearizationTransformerParametric statisticsComputer scienceSmall-signal modelElectric power systemElectrical impedanceElectronic engineeringNetwork topologyRectifier (neural networks)Control theory (sociology)EngineeringPower (physics)Artificial neural networkElectrical engineeringVoltageNonlinear systemMathematics

Abstract

fetched live from OpenAlex

This paper presents average-value modeling of the synchronous generator transformer-isolated power supply, which is a part of a reduced-scale benchmark system representative of future electric ships. Developing such models using traditional analytical approaches is very challenging due to complicated system topologies and the existence of multiple switching patterns of the rectifier circuit. This paper considers an alternative computer-aided approach where the key parametric functions are established numerically. The proposed average-value model is shown to be very accurate in predicting the large-signal transients as well as for linearization and subsequent small-signal analysis, e.g. determining output impedance transfer function.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.999

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)0.0020.000

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 head0.012
GPT teacher head0.241
Teacher spread0.228 · 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 designBench or experimental
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

Citations3
Published2012
Admission routes1
Has abstractyes

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