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Impact of Dead-band Time on the Harmonic Spectrum of Power Converters

2020· article· en· W3127495728 on OpenAlexaff
Jigneshkumar Patel, Vijay K. Sood

Bibliographic record

Venue2020 IEEE Electric Power and Energy Conference (EPEC) · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsConvertersPower (physics)HarmonicHarmonic analysisHarmonic spectrumElectronic engineeringElectrical engineeringComputer sciencePhysicsHarmonicsEngineeringVoltageAcoustics

Abstract

fetched live from OpenAlex

Power electronic (PE) converters suffer from a topological disadvantage as they often employ two series-connected switches within a common leg of a converter. These two switches have a complementary role i.e. when one switch is ON, the other must be OFF otherwise a short circuit will result with impending risks for the switches and power supply. This condition must be respected at all times - during steady-state and transient switching periods. Although modern PE switches are very fast switching, their speed is not infinite. And, in practice, a suitable dead-band time is required with any pulse width modulation (PWM) technique to prevent the occurrence of a short circuit between two switches in a common leg. A larger dead-band time enhances the security of operation of the switches (converter) but at the expense of additional harmonics and lower efficiency. This paper investigates the impact of dead-band time on the harmonic spectrum for a single-phase H-bridge converter for low/medium voltage applications and cascaded H-bridge (CHB) multilevel converter for medium/high voltage applications.

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)0.001
metaresearch head score (Gemma)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.197
Teacher spread0.189 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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