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Record W2112830211 · doi:10.1109/ccece.2005.1556999

Harmonic current reduction for a pwm rectifier with very low carrier ratio in a microturbine system

2006· article· en· W2112830211 on OpenAlexaff
Kai Zhang, Liuchen Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPulse-width modulationTotal harmonic distortionRectifier (neural networks)PWM rectifierHarmonicInverterGenerator (circuit theory)LC circuitControl theory (sociology)Band-stop filterPrecision rectifierPower factorHarmonicsHarmonic spectrumElectronic filterElectrical engineeringComputer scienceElectronic engineeringEngineeringPower (physics)PhysicsFilter (signal processing)CapacitorLow-pass filterAcousticsVoltage

Abstract

fetched live from OpenAlex

When a PWM rectifier is connected to the generator in a microturbine power generation system to form a "back-to-back" configuration together with a grid-connected PWM inverter, the rectifier will have to operate with a very low PWM carrier ratio, since the generator output frequency can be as high as of kHz level while the switching frequency of semiconductors are limited. A method to reduce the distortion of the generator side currents in such a case is proposed, featuring a carrier factor of 5; a modified SPWM scheme to eliminate the 3/sup rd/ order harmonic and its multiples from the current spectrum; and an LC-resonance based notch filter to remove the remaining most significant harmonic component. Simulation results show that near perfect sinusoidal generator current can be obtained with this 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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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 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

Citations2
Published2006
Admission routes1
Has abstractyes

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