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Record W2171112277 · doi:10.1109/iecon.2005.1568960

Dual 18-pulse rectifier for high-power multilevel inverters

2005· article· en· W2171112277 on OpenAlexaff
Zhi Cheng, Bin Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHarmonicsTotal harmonic distortionTransformerTopology (electrical circuits)WaveformElectronic engineeringPulse-width modulationRectifier (neural networks)VoltageHarmonic analysisElectrical engineeringThyristorComputer scienceControl theory (sociology)Engineering

Abstract

fetched live from OpenAlex

This paper presents a new rectifier topology that uses 18-pulse transformers to achieve 36-pulse harmonic cancellation. The proposed rectifier can be used to reduce the input current harmonics for several multilevel inverters, such as 3-level neutral point clamped (NPC), 5-level cascaded H-bridge (CHB) and 5-level NPC/H-bridge inverters. It can also be used to neutralize harmonics between two 18-pulse drives. Compared to conventional multi-pulse topologies, it uses less secondary windings and simpler transformers to achieve better current THD. Fourier analysis shows that the topology is able to achieve 36-pulse harmonic cancellation. It also provides additional harmonic cancellation for the 5/sup th/ and 7/sup th/ harmonics for non-ideal transformers or between two drives. The simulated current waveforms, current spectra and DC bus voltages are given to confirm the theoretical analysis. Experiments are performed to verify the performance.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.021
GPT teacher head0.229
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
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

Citations6
Published2005
Admission routes1
Has abstractyes

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