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Record W2907682852 · doi:10.1109/iecon.2018.8591854

An Exact Time Domain Analysis of DCM Boost Mode LLC Resonant Converter for PV applications

2018· article· en· W2907682852 on OpenAlexaff
A. K. Awasthi, Snehal Bagawade, Amit Kumar, Praveen Jain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsInductorCapacitorVoltageTransformerControl theory (sociology)DiodeBoost converterElectronic engineeringComputer scienceTopology (electrical circuits)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The search for a cost feasible, optimal design of LLC converter for wide input voltage range conditions is inhibited by errors in steady state values of peak gain, tank current, tank voltage, active power etc., due to various approximations in the analysis. This leads to erroneous selection of circuit parameters. The present work provides an exact, approximation free time domain mathematical model of LLC converter operating in transformer secondary current discontinuous mode (DCM) below resonance in boost mode. The proposed approach defines voltage gain as a function of relative switching frequency ω and t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> , which represents the rectifier diode to switch conduction ratio for DCM operation. Existing works have defined voltage gain as an explicit function of quality factor (Q) and ω. The current approach leads to accurate derivation of converter voltage gain, tank RMS current, tank capacitor voltage, zero voltage switching (ZVS) angle etc. Analytical expressions are validated with simulation studies using PSIM for various inductor ratio values. A final design is verified on an experimental prototype of 320W output LLC converter designed for an input range of 20-40V for solar PV applications.

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: Methods · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.530

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 head0.257
Teacher spread0.250 · 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
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

Citations15
Published2018
Admission routes1
Has abstractyes

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