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Record W2755743537 · doi:10.1109/tec.2017.2752129

Generalized Parametric Average-Value Model of Line-Commutated Rectifiers Considering AC Harmonics With Variable Frequency Operation

2017· article· en· W2755743537 on OpenAlexaff
Seyyedmilad Ebrahimi, Navid Amiri, Hamid Atighechi, Yingwei Huang, Liwei Wang, Juri Jatskevich

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2017
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsBC Hydro (Canada)Okanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsHarmonicsThyristorRectifier (neural networks)Parametric statisticsControl theory (sociology)Computer scienceElectronic engineeringPower (physics)Line (geometry)CommutationNonlinear systemParametric modelElectric power systemVoltageEngineeringElectrical engineeringMathematicsPhysicsControl (management)

Abstract

fetched live from OpenAlex

Line-commutated rectifiers are often utilized in machine-converter systems and many energy conversion applications. Simulation of such power systems using detailed switching models of rectifiers is computationally expensive, and as an alternative for system-level studies, the so-called average-value modeling (AVM) techniques have become indispensable. The parametric AVM (PAVM) uses a computerized approach for establishing the key relationships between the averaged ac and dc variables. In this paper, a generalized PAVM (GPAVM) is proposed, which extends several previously proposed models. The new GPAVM includes the ac harmonics in thyristor-controlled rectifier models considering their nonlinear dependency on the line frequency. The new model is verified using detailed simulations and experimental results and is demonstrated to have better accuracy in a wider range of operating conditions and speeds/frequencies.

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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.027
GPT teacher head0.223
Teacher spread0.195 · 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
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

Citations34
Published2017
Admission routes1
Has abstractyes

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