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Record W3007479663 · doi:10.1109/tte.2020.2976878

Emulation of Loss Free Resistor for Single-Stage Three-Phase PFC Converter in Electric Vehicle Charging Application

2020· article· en· W3007479663 on OpenAlexaff
Nupur Rathore, Sivanagaraju Gangavarapu, Akshay Kumar Rathore, Deepak Fulwani

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsResistorRobustness (evolution)ConvertersRectifier (neural networks)Power factorControl theory (sociology)Topology (electrical circuits)EmulationComputer scienceElectric vehicleModular designElectronic engineeringVoltageElectrical engineeringPower (physics)EngineeringPhysics

Abstract

fetched live from OpenAlex

In this article, the modular three-phase ac-dc converter using single-phase isolated Ćuk rectifier modules for charging of electric vehicles is discussed. The converters are designed and analyzed for the continuous conduction mode (CCM). This article is based on a new concept of adaptive sliding-mode-based loss-free resistor (ASLFR). ASLFR is a control scheme, which allows dual aim of power factor correction along with tight voltage regulation; hence, the adopted topology serves as a fitting single-stage solution for balanced three-phase systems. Complete theory is developed for the application, and the effectiveness of the scheme is well-established. Various studies to confirm the robustness of the system to any load and line variation are carried out. Moreover, a qualitative analysis is also made to show the expediency of the proposed ASLFR. Simulation as well as experimental studies are claimed theoretically.

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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.239
Teacher spread0.219 · 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
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

Citations15
Published2020
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Transportation ElectrificationSame topicAdvanced DC-DC ConvertersFrench-language works237,207