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Record W2084739851 · doi:10.1109/iecon.2013.6699647

A hardware-efficient programmable two-band controller for PFC rectifiers with ripple cancellation circuits

2013· article· en· W2084739851 on OpenAlexaff
Behzad Mahdavikhah, S. M. Ahsanuzzaman, Aleksandar Prodić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRipplePower factorController (irrigation)CapacitorTotal harmonic distortionComputer scienceControl theory (sociology)Rectifier (neural networks)Electronic engineeringVoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper introduces a hardware-efficient controller for rectifiers with power factor correction (PFC) that feature circuits for cancellation of the output voltage ripple at two times the line frequency. It provides simultaneous and independent regulation of the ripple amplitudes and dc voltage values for both the PFC stage and the cancellation circuit, allowing for the tradeoff management between the overall system volume and its efficiency. Unlike previous solutions, the controller does not require costly components for obtaining information about the output current, converter parameters, or phase and frequency of the voltage ripple. These advantages are obtained through the hardware sharing and use of a novel two-band compensator. The effectiveness of the controller is demonstrated on a 45 W programmable-output flyback based PFC rectifier prototype with a buck-boost based ripple cancellation circuit. The results demonstrated achieves a 1% ripple for the full range of operating conditions with maximum of 90% ripple reduction while utilizing a 500uF output capacitor for PFC, providing high power factor and low harmonic distortion.

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: none
Teacher disagreement score0.965
Threshold uncertainty score0.564

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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