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Record W3141523613 · doi:10.1109/tpel.2021.3070885

Improved Harmonic Profile for High-Power PWM Current-Source Converters With Modified Space-Vector Modulation Schemes

2021· article· en· W3141523613 on OpenAlexaff
Pengcheng Liu, Zheng Wang, Yang Xu, Zhixiang Zou, Fujin Deng, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsPulse-width modulationHarmonicsDwell timeConvertersSpace vector modulationElectronic engineeringControl theory (sociology)HarmonicPower (physics)Modulation (music)Harmonic analysisComputer scienceSampling (signal processing)Voltage sourceTotal harmonic distortionSupport vector machineVoltageEngineeringPhysicsTelecommunicationsElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Pulsewidth-modulated (PWM) current-source converters (CSCs) have the features of simple configuration, reliable short-circuit protection, and sinusoidal ac output voltages. The conventional space-vector modulation (SVM) scheme usually generates high-magnitude low-order harmonics in CSCs with low switching frequency. In this article, two modified SVM schemes, namely MSVM1 and MSVM2, are proposed to improve the harmonic performance for PWM CSCs in the high-power applications with low switching frequencies. The MSVM1 scheme focuses on eliminating the average dwell-time error of current vectors within each sampling period, while the MSVM2 scheme is targeted to achieve the accurate dwell time of current vectors as that of the natural sampling-based SVM scheme. The theoretical analysis and the calculation equations of dwell time are derived for both MSVM1 and MSVM2. The experiments are given to verify the effectiveness of the two modified SVM schemes in improving the harmonic performance of high-power CSCs with low switching frequencies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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.

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

Citations31
Published2021
Admission routes1
Has abstractyes

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