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Record W2170480450 · doi:10.1109/isie.2009.5214712

Three-phase four-wire Vienna I rectifier with active filter function including neutral current mitigation

2009· article· en· W2170480450 on OpenAlexaff
Bachir Kedjar, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsHarmonicsControl theory (sociology)AC powerRectifier (neural networks)PWM rectifierThree-phaseVoltagePulse-width modulationPower factorPower (physics)Active filterCompensation (psychology)EngineeringComputer scienceElectronic engineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

In this paper a Vienna I rectifier is used to achieve active filtering function including the neutral wire current compensation (VR-AF) in a three-phase four-wire system. A linear quadratic with integral action regulator (LQIR) is designed to achieve overall DC bus voltage regulation, harmonics, reactive power and load's neutral current compensations. The converter model is set in the d-q-o rotating reference frame. The latter is augmented with the integral of the q and o components of the (VR-AF) currents and overall DC bus voltage to achieve integral action. The converter is controlled as a multi-input multi-output (MIMO) system and a fixed PWM at 10 kHz is used to generate the gating signals of the power devices. The system is tested for harmonics, reactive power and load unbalance compensation for balanced/unbalanced loads. The simulation results obtained with SPS and Simulink of Matlab show good performance in terms of overall DC bus voltage regulation; line currents shaping, balancing and neutral current mitigation which proved the effectiveness of the adopted control strategy.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.061
GPT teacher head0.291
Teacher spread0.230 · 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
GenreMethods

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

Citations6
Published2009
Admission routes1
Has abstractyes

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