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Record W4205375673 · doi:10.1109/tia.2021.3138035

A Carrier-Based Modulation Strategy for Modular Isolated Matrix Rectifiers

2021· article· en· W4205375673 on OpenAlexafffund
Fanxiu Fang, Yuzhuo Li, Hao Tian, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsGalvanic isolationModulation (music)Electronic engineeringComputer scienceModular designRectifier (neural networks)Pulse-width modulationVoltageEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

An isolated matrix rectifier is promising as it can provide galvanic isolation and maintain high power density due to the high-frequency link when compared with dc link in voltage source converter based solutions. To increase power rating or extend the output dc voltage range, this article proposes the input-parallel–output-parallel (IPOP) and input-parallel–output-series (IPOS) modular structures. These modular structures can achieve favorable features such as modularity and scalability without circulating current issues. To facilitate the operation of an IPOS- or IPOP-connected isolated matrix rectifier, this article proposes a carrier-based modulation strategy. Compared to space vector modulation methods, the proposed carrier-based modulation strategy is preferable due to the low complexity even under a high number of modules. The proposed modulation method can well compensate for the duty cycle loss to reduce current harmonics. Compared to conventional modulation methods, the narrow pulse is reduced to achieve high current quality even at a low modulation index. Moreover, the common-mode voltage is reduced by 50%. Simulation and experimental results are provided to validate the feasibility and performance of the proposed carrier-based modulation method.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.969

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)0.0000.000

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 head0.270
Teacher spread0.241 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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