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Record W3015751225 · doi:10.1109/tie.2020.2984432

A Soft-Switched, Multiport Photovoltaic Power Optimizer With Integrated Storage Interface and Output Voltage Regulation

2020· article· en· W3015751225 on OpenAlexafffund
Sanjida Moury, John Lam

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsYork University
FundersOntario Research Foundation
KeywordsMaximum power point trackingPhotovoltaic systemBattery (electricity)Boost converterVoltagePower (physics)EngineeringElectronic engineeringMaximum power principleElectrical engineeringComputer science

Abstract

fetched live from OpenAlex

This article proposed a multiport converter (MPC) configuration for a photovoltaic (PV) battery system. In the proposed design, the battery charging and discharging circuits are integrated with the PV power optimizer through high-frequency ac link. The proposed converter system is capable of regulating the output voltage, following battery charging profile and tracking the maximum power point (MPP) of each PV module. Variable switching frequency is used to achieve maximum power point tracking in this design. All the switches of the converter are able to achieve soft-switching turn-on and turn-off at different operating conditions. The operating principles and the control strategy of the proposed converter are presented. Simulation results and experimental results on a 175-W system are presented to validate the features of the proposed converter.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.209
Teacher spread0.191 · 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 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 routes2
Has abstractyes

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