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Record W2570120095 · doi:10.1109/iecon.2016.7793585

A high-efficiency multi-port DC-DC converter for photovoltaic energy conversion systems

2016· article· en· W2570120095 on OpenAlexaff
Sebastian Rivera, Mike K. Ranjram, Peter W. Lehn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterfacingPhotovoltaic systemBuck converterComputer sciencePort (circuit theory)Buck–boost converterTopology (electrical circuits)Flyback converterElectronic engineeringPower (physics)Boost converterElectrical engineeringVoltageRange (aeronautics)Forward converterEngineeringPhysics

Abstract

fetched live from OpenAlex

The technical challenges associated with distributed generation along with the increased adoption of electric vehicles has motivated the development of new configurations for dc microgrids. In this paper, a novel unidirectional multi-port power converter acts as the building block for interfacing different configurations of PV energy conversion systems, allowing it to reach high efficiency for a wide range of input power. Additionally, depending on the connection of the sources, the converter can act as a buck-boost converter or a buck converter, where the former offers wider input voltage range and the latter offers higher efficiency. Additionally, a sum/difference control scheme suitable for both configurations is presented. Simulation results are provided for a 32 kW PV system, establishing a comparison between the two configurations and validating the proposed topology and control scheme.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.237
Teacher spread0.220 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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