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A Modular Single-Stage PV Step-up Converter With Integrated Power Balancing Feature Using Inter-Coupled Active Voltage Quadruplers

2022· article· en· W4280590211 on OpenAlexaff
Kajanan Kanathipan, John Lam

Bibliographic record

Venue2022 IEEE Applied Power Electronics Conference and Exposition (APEC) · 2022
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsYork University
Fundersnot available
KeywordsMaximum power point trackingBoost converterComputer scienceVoltagePhotovoltaic systemInductorModular designElectronic engineeringDuty cycleBuck converterEngineeringControl theory (sociology)Electrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This paper presents a novel modular single-stage photovoltaic step-up converter with an integrated power balancing technique that features high frequency inter-connecting active voltage quadruplers (VQ). The input stage of each module consists of an integrated boost circuit with a CLL resonant circuit, allowing for both maximum power point tracking (MPPT) and soft-switching operation to be achieved. To achieve balanced output voltage for all the modules while providing MPPT, the output inductor of the resonant circuit in each module is coupled with the neighboring module with duty ratio control of the active VQ circuit is used to control the output voltage. The proposed technique allows the entire PV converter system to simultaneously achieve: (1) MPPT, (2) soft-switching operation for all modules, and (3) equal output voltage and power distribution without the use of additional circuitry. Descriptions of the proposed converter and the proposed power balancing technique are provided. Results on a 2kW, 3kV-output system is provided to highlight the features of the proposed converter configuration.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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