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Compensation of Grid Current Distortion at Sector Boundaries in SVM for PFC Three-Phase Isolated PWM Buck Rectifier

2022· article· en· W4361791526 on OpenAlexaff
Parth Patel, Narsa Reddy Tummuru, Ambrish Chandra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPower factorRectifier (neural networks)Control theory (sociology)Pulse-width modulationAC powerThree-phaseDuty cyclePWM rectifierBuck converterSpace vector modulationCompensation (psychology)Computer scienceDistortion (music)VoltageElectronic engineeringEngineeringElectrical engineeringCMOSAmplifierArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes a modified modulation concept for three-phase isolated PWM buck rectifier during phase shift operation of input currents with respect to the mains voltage up-to <tex>$\pm 30^{\circ}$</tex> with compensation of grid current distortion at sector boundaries. The proposed modulation scheme based on space vector modulation (SVM) also allows generation of reactive power to compensate for the input displacement factor due to grid-side filter and maintain unity power factor at light load conditions. The paper starts with a review of converter operation with SVM. Then, output voltage of converter is derived for adjustable power factor operation and generalized duty-cycle loss compensation technique is discussed which is also applicable to phase-shift operation. The implementation of the proposed modulation scheme is discussed and its effectiveness is demonstrated through simulation results for 400 V (RMS), 50 Hz three-phase voltage to 200 V DC power converter of output power up to 6 kW.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.934

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)0.0010.000

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 head0.029
GPT teacher head0.254
Teacher spread0.225 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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