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Record W1972657102 · doi:10.1109/isie.2010.5637255

A vector controlled single-phase voltage source inverter with enhanced dynamic response

2010· article· en· W1972657102 on OpenAlexaff
Nayeem Ninad, Luiz A. C. Lopes, Alfred Rufer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)InverterConvertersTransformation (genetics)Steady state (chemistry)Computer scienceController (irrigation)Line (geometry)VoltageReference frameThree-phasePhase (matter)DC biasFrame (networking)MathematicsEngineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

Vector (dq) control is a powerful technique used in high dynamic performance inverters. The transformation of ac into dc quantities allows the controller to be designed as for dc-dc converters, with infinite gain at the steady-state operating point for zero steady-state error. It can be easily implemented for three-phase inverters but presents some challenges for single-phase ones. The orthogonal component (β) required for the stationary to rotating frame transformation does not exist and is usually created by phase shifting the real one (α) by ¼ of a line cycle, what deteriorates the dynamic response. Recently an approach where the orthogonal quantity is obtained by emulating a fictitious orthogonal circuit has been proposed. This paper shows that the results obtained with this new approach are better than with the ¼ line cycle delay and that the α and β components become identical to those of a three-phase inverter. Simulation and experimental results are presented.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.006
GPT teacher head0.209
Teacher spread0.204 · 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

Citations15
Published2010
Admission routes1
Has abstractyes

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