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Frequency Domain Modelling of an LCLC Resonant Converter with Capacitive Output Filter Under Hybrid Modulation

2021· article· en· W3215914753 on OpenAlexaff
Shahbaj Dhillon, A. K. Awasthi, Praveen Jain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsHarmonicsDuty cycleTransformerElectronic engineeringFrequency domainCapacitive sensingWaveformHarmonic analysisHarmonicFrequency modulationControl theory (sociology)Topology (electrical circuits)EngineeringVoltageComputer sciencePhysicsElectrical engineeringRadio frequencyAcoustics

Abstract

fetched live from OpenAlex

The LCLC resonant converter topology is popular and frequently used in high voltage applications as it can more accurately describe the effect of parasitic elements in high turns ratio transformers over LLC or LCC. The accuracy of the Fundamental Harmonic Approximation (FHA) analysis technique suffers from low-quality factor (Q) designs which is desired to minimize component stress. Presented in this paper is a new frequency domain model incorporating effects of both input and load side harmonics under a duty cycle-variable frequency-based hybrid modulation technique previously shown to be advantageous under light load conditions. Generalized curves of converter resonant tank gain describe its operation. Steady-state waveforms from the model and a 400V, 250W experimental prototype is used to verify the accuracy

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.013

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.200
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 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
GenreMethods

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

Citations1
Published2021
Admission routes1
Has abstractyes

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