MétaCan
Menu
Back to cohort
Record W1482142741 · doi:10.1109/compel.2015.7236489

Parametric average value modeling of high power AC/AC cyclo converters

2015· article· en· W1482142741 on OpenAlexaff
Seyyedmilad Ebrahimi, Navid Amiri, Hamid Atighechi, Juri Jatskevich, Liwei Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsConvertersThyristorParametric statisticsComputer sciencePower (physics)Electronic engineeringTransient (computer programming)VoltageControl theory (sociology)EngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Line-commutated thyristor-controlled AC/DC and AC/AC converters are commonly used in high power industrial applications. Therefore, analyzing industrial distribution systems requires accurate and computationally efficient models that may be easily implemented in simulation software packages. Average-value models (AVMs) avoid the discrete switching of the converters, provide accurate system-level steady-state and transient simulation results, which make such models particularly suitable for large scale studies of power electronic systems. Parametric average value modeling (PAVM) is one of very promising techniques resulting in accurate models that are also considerably simpler in terms of mathematical complexity. The PAVMs for AC/DC converter systems have been thoroughly analyzed in literature. This paper proposes a new PAVM for AC/AC cyclo-converters which supply the load at lower AC frequencies. The superior performance and computational advantages of the proposed PAVM of cyclo-converters are verified against the detailed model in terms of CPU efficiency as well as numerical accuracy.

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 score0.047
Threshold uncertainty score0.697

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 head0.024
GPT teacher head0.216
Teacher spread0.192 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicSilicon Carbide Semiconductor TechnologiesFrench-language works237,207