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Record W4392122996 · doi:10.1109/jestpe.2024.3369534

High-Frequency Interlinking High-Voltage Gain PV Converter Modules With Embedded Power Balancing Technique for DC-Distributed System

2024· article· en· W4392122996 on OpenAlexafffund
Kajanan Kanathipan, John Lam

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrical engineeringPower (physics)Electronic engineeringVoltageComputer scienceSwitched-mode power supplyMaterials scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

A new high-frequency (HF) interlinking photovoltaic (PV) step-up converter modules utilizing an embedded power balancing technique while featuring individual single-sensor maximum power point tracking (MPPT) is presented in this article. An integrated boost-capacitor–inductor–inductor (CLL) resonant circuit at the input allows for both MPPT and soft-switching operation at all conditions for each module while HF coupled output inductors together with the proposed active voltage quadrupler (AVQ) allows for regulated power flow between HF interlinking modules. The proposed technique allows the modular PV converter system to achieve simultaneous MPPT, soft-switching operation, and equal power distribution without the use of additional circuitry. The HF interlinking nature of the system allows for additional modules to be added without requiring parameter changes for existing modules. Analysis of the proposed converter system is discussed in this article. The performance of the proposed PV power balancing technique is validated through simulation results on a modular 13-kW, 10-kV output system and hardware experimental results on a scaled-down modular 500-W, 850-V output prototype system.

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 score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.214
Teacher spread0.211 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

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