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Record W2019218244 · doi:10.1109/tpwrd.2014.2342240

Dynamic Average-Value Modeling of Direct Power-Controlled Active Front-End Rectifiers

2014· article· en· W2019218244 on OpenAlexaff
José M. Cano, Juri Jatskevich, Joaquín G. Norniella, Ali Davoudi, Xin Wang, J.A. Martínez, Ali Mehrizi‐Sani, Maryam Saeedifard, Dionysios Aliprantis

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2014
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsCarleton UniversityUniversity of British Columbia
Fundersnot available
KeywordsRectifier (neural networks)Transient (computer programming)AC powerEngineeringPower factorElectronic engineeringElectric power systemPower (physics)VoltageFront and back endsPower controlComputer scienceControl theory (sociology)Electrical engineeringControl (management)Mechanical engineering

Abstract

fetched live from OpenAlex

Active front-end (AFE) rectifiers are becoming widely used in medium-to-high-power adjustable speed drives (ASDs) to achieve regenerative operation and meet the energy efficiency and harmonic requirements. The typical control methods used with AFE rectifiers include voltage-oriented control (VOC), direct power control (DPC) and virtual-flux-based methods. This paper presents a dynamic average-value model (AVM) of the AFE rectifier system which is based on the voltage-source converter (VSC) operated with DPC. The developed AVM and the modeling methodology presented in this paper highlight the specifics of the hysteresis bang-bang type DPC that is used to track the real and reactive power references. The proposed AVM of the AFE rectifier system is demonstrated to significantly reduce the computing effort while accurately capturing the fast transient performance of the system.

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 categoriesMeta-epidemiology (narrow)
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 score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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 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

Citations15
Published2014
Admission routes1
Has abstractyes

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