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Record W2039124308 · doi:10.1109/icit.2006.372728

Modeling and Control of a Single-Phase Sheppard-Taylor Based Power Factor Corrector

2006· article· en· W2039124308 on OpenAlexaff
Hadi Y. Kanaan, Alfred Hayek, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsTotal harmonic distortionPower factorControl theory (sociology)Pulse-width modulationBuck converterRobustness (evolution)Voltage sourceVoltageWaveformTopology (electrical circuits)Computer scienceElectronic engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, a single-phase power factor corrector (PFC) based on the Sheppard-Taylor topology is presented. Compared to conventional buck, boost or buck-boost PFCs, this topology allows a better current tracking at the AC side, with a relatively reduced voltage at the DC side. Consequently, the high frequency AC filters required by the buck PFCs are avoided, and the voltage stresses on the boost switches are significantly reduced. Furthermore, the control detuning phenomenon from which suffer most of the conventional PFCs, especially at very low input voltage, is avoided. This yields major improvements in the source current waveform. The proposed converter is integrated as a PFC at the DC-end of a single-phase diode bridge. A pulse-width-modulated (PWM) control is developed in order to ensure a unity power factor at the AC-source side and a regulated voltage at the DC-load side. In order to verify the performance of the proposed control scheme, simulation experiments are carried out on a numerical version of the converter with its control circuit. The implemented model of the converter is obtained by using the switching function technique. The control system is tested under both rated and disturbed operating conditions. The system performance is evaluated in terms of source current total harmonic distortion (THD), voltage regulation, robustness and dynamic time response to a set point offset.

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 score0.734
Threshold uncertainty score0.607

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 head0.009
GPT teacher head0.209
Teacher spread0.199 · 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

Citations23
Published2006
Admission routes1
Has abstractyes

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