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Record W2904889304 · doi:10.1109/ecce.2018.8558166

A Double-Input Photovoltaic Inverter System with a Soft-Switched Magnetically Coupled AC/DC Bidirectional Circuit for Energy Storage Application

2018· article· en· W2904889304 on OpenAlexaff
Joanne Hui, Praveen Jain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhotovoltaic systemInverterRectifier (neural networks)Computer scienceBoost converterEnergy storageĆuk converterElectrical engineeringVoltageElectronic engineeringEngineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

This paper presents a double-input solar inverter system with a magnetically coupled AC/DC soft-switched bidirectional converter unit for energy storage application. The presented double-input front-end converter consists of an integrated DC/DC SEPIC and Cuk type circuit with a shared output filter, while the magnetically coupled bi-directional AC/DC converter is a current-fed full-bridge circuit. The presented bi-directional converter unit is able to achieve zero current switching (ZCS) condition when it operates in rectifier mode (i.e. the energy storage unit in charging condition) and is able to achieve zero voltage switching (ZVS) turn-on with ZCS turn-off in its inverter mode (i.e. when supplying energy to the load). Due to the soft-switching function provided by the bidirectional converter interface and the use of a double-input PV converter with an integrated output filter, the proposed circuit is compact and is well suited for residential solar energy conversion applications. The operating principles of the proposed system will be discussed in this paper. Results are given on a 300W double-input PV inverter system to highlight the merits of this work.

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 score0.968
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 integrity0.0000.000
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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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