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Mitigating the dead-time effects on harmonics spectrum of inverter waveform by the confined band VSFPWM technique

2021· article· en· W3110240811 on OpenAlexaff
Hussain Attia, Hang Seng, Ahmad Elkhateb

Bibliographic record

VenueInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsQueen's University
FundersEngineering and Physical Sciences Research Council
KeywordsHarmonicsInverterPulse-width modulationTotal harmonic distortionWaveformDead timeHarmonic spectrumPower (physics)VoltageModulation (music)MATLABControl theory (sociology)HarmonicDistortion (music)Computer scienceElectronic engineeringPhysicsElectrical engineeringTelecommunicationsAcousticsEngineeringBandwidth (computing)

Abstract

fetched live from OpenAlex

The dead-time is necessary to be inserted between the gates drive pulses of the two power electronic switches in a one leg of any inverter to avoid a short circuit in the leg and the DC supply as well. However, adding the dead-time increases the low order harmonics of the output voltage/current waveform of the inverter. This paper investigates the positive effects of decreasing the pulse width modulation (PWM) drive pulses number per fundamental period on the current low order harmonics. In addition, this paper evaluates the impact of the confined band variable switching frequency pulse width modulation (CB-VSFPWM) technique on inverter performance in terms of dead-time mitigating, and consequenctely lowering the low order harmonics. CB-VSFPWM technique reduces the total harmonic distortion (THD) levels in the inverter output current as well. Theoretical analysis of the CB-VSFPWM effectiveness in reducing the negative effect of the dead-time has explained in this study and confirmed by the MATLAB/Simulink simulation results.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.632

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 science0.0010.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.183
Teacher spread0.180 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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