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Record W1550381683 · doi:10.1109/ciep.2004.1437533

Multipulse diode rectifiers for high-power multilevel inverter fed drives

2005· article· en· W1550381683 on OpenAlexaff
Bin Wu, Yunwei Li, Sanmin Wei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTotal harmonic distortionRectifier (neural networks)Computer scienceInverterHarmonicDiodePower (physics)Electronic engineeringVoltagePower factorHarmonic analysisSet (abstract data type)Line (geometry)Electrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

In an effort lo satisfy stringent harmonic requirements set by North American and European standards such as IEEE standard 519-1992, major medium voltage (MV) drive manufacturers around world often use multipulse diode rectifiers in their drives as a front end. The most widely used multipulse rectifier configurations are 12-, 18- and 24-pulse rectifiers, which can be further classified into series and separate types for use in various multilevel inverter fed drives. However, a systematic analysis on these rectifiers seems not reported in the literature. In this paper, a comprehensive analysis on the line current THD and input power factor of multipulse rectifiers is carried out through computer simulation and experiments, and results are presented in a graphical format for ease of use. The paper serves as a good technical reference for practicing engineers and academic researchers as well.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.216
Teacher spread0.208 · 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 designBench or experimental
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

Citations19
Published2005
Admission routes1
Has abstractyes

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