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A High Voltage Gain Quadratic Boost Converter using a Voltage Doubler and Voltage-Lift Technique

2020· article· en· W3019516132 on OpenAlexaff
Arpan Laha

Bibliographic record

Venue2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020) · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsVoltage doublerBoost converterVoltage multiplierVoltageVoltage dividerElectrical engineeringVoltage regulationVoltage optimisationHigh voltageDropout voltageEngineeringElectronic engineeringComputer science

Abstract

fetched live from OpenAlex

A novel high voltage gain quadratic boost converter is proposed which is based on the voltage-lift technique and use of industrial applications including UPS (uninterrupted power supplies), PV (photovoltaic) systems, automobile HID lamps, fuel cells, etc. The voltage-lifts a voltage doubler cell. The proposed converter can be used in a wide range cells used instead of inductors in the quadratic boost circuit increase the voltage gain four times. The voltage doubler cell doubles the resulting voltage gain and alleviates the voltage stress across the power switch by reducing it to half the output voltage. Therefore, a low on state resistance of the power switch is required which thereby reduces the conduction losses. The operating principle has been shown and the steady-state analysis of the proposed converter has been discussed in detail. Finally the theoretical results have been verified by simulations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.230
Teacher spread0.215 · 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 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

Citations10
Published2020
Admission routes1
Has abstractyes

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Same venue2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020)Same topicAdvanced DC-DC ConvertersFrench-language works237,207