MétaCan
Menu
Back to cohort

Variable Frequency-Duty Cycle Modulation Technique for Light Load Efficiency Improvement of LLC Resonant Converter for Wide Input Voltage Range in PV Applications

2019· article· en· W3005367732 on OpenAlexaff
A. K. Awasthi, Snehal Bagawade, Praveen Jain

Bibliographic record

Venue2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE) · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsDuty cycleVoltagePulse-frequency modulationModulation (music)Resonant converterSwitching frequencyFrequency modulationRange (aeronautics)Resonant inverterInverterMaterials scienceControl theory (sociology)Electronic engineeringConvertersComputer scienceElectrical engineeringPhysicsEngineeringAmplitude modulationRadio frequencyAcoustics

Abstract

fetched live from OpenAlex

LLC type series resonant converter has gained prominence for applications which require zero voltage switching (ZVS) for wide input and load range variation. However, conversion efficiency under constant switching frequency modulation (FM) degrades at light-load conditions. Switching losses (due to loss of ZVS and turn -off) dominates the total losses. This is due to a wide range of switching frequencies required for output voltage regulation. Turn-off losses also increase significantly at high switching frequencies. The proposed modulation technique narrows the switching frequency range while, while ensuring tight output voltage regulation. and reduces the effective duty ratio. Furthermore, the structure of the converter doesn't require any modification during the design procedure. An analytical time domain mathematical model has been derived for the proposed technique for accurate voltage gain calculations. An experimental prototype of 20-40V input, 380V/300W output LLC resonant converter for solar micro-inverter application has been built to validate the proposed modulation technique by significantly improving light load efficiency.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.203
Teacher spread0.198 · 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.

Study designBench or experimental
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

Citations6
Published2019
Admission routes1
Has abstractyes

Explore more

Same venue2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE)Same topicAdvanced DC-DC ConvertersFrench-language works237,207