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Record W2972453930 · doi:10.1109/tpel.2019.2941387

A Novel Dual-Input High-Gain Transformerless Multilevel Single-Phase Microinverter for PV Systems

2019· article· en· W2972453930 on OpenAlexaff
Eltaib Abdeen D. Ibrahim, Mahmoud A. Gaafar, Mohamed Orabi, Ahmed Sheir, Mohamed Z. Youssef

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSolar micro-inverterPhotovoltaic systemTopology (electrical circuits)ConvertersCapacitorModular designNetwork topologyElectronic engineeringComputer scienceInverterMaximum power point trackingVoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This article proposes a novel single-phase microinverter that is fit to process the power of two photovoltaic (PV) modules in a modular way. The proposed topology combines a full-bridge inverter integrated with two dc-dc boost converters, in addition to a dc link that consists of switched capacitor (SC) networks. The operating modes of the proposed topology are illustrated. The voltage stress of all components is identified. A modulation technique along with a control system is developed for a proper operation of the proposed topology. A comparative study with other topologies is introduced, and the following merits for the proposed one are presented: 1) The power of two PV modules can be harvested individually or simultaneously without any circulating current issues; 2) very high gain can be acquired, and, thus, no series connection of PV modules is required for grid-tied applications; 3) transformerless operation; 4) multilevel shaping of the output voltage, and, thus, reduced filter size is required; and 5) self-balancing for the dc-link capacitors, and, thus, simple control systems can be used. The performance of the seven-level version of the proposed topology is validated using real-time simulation and experimental prototype under grid-tied and stand-alone conditions, respectively.

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 score0.937
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
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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