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Record W2795772095 · doi:10.1109/jestpe.2018.2821663

Modified Seven-Level Pack U-Cell Inverter for Photovoltaic Applications

2018· article· en· W2795772095 on OpenAlexaff
Hani Vahedi, Mohammad Sharifzadeh, Kamal Al‐Haddad

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPhotovoltaic systemInverterMaximum power point trackingGrid-tie inverterConvertersVoltageElectrical engineeringTopology (electrical circuits)Power (physics)Electronic engineeringComputer scienceGrid connectionEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper proposes a modified conation of single-phase pack U-cell (PUC) multilevel inverter in which the output voltage has higher amplitude than the maximum dc link value used in the topology as a boost operation. The introduced inverter generates seven-level ac voltage at the output using two dc links and six semiconductor switches. Comparing to cascaded H-bridge and neutral point clamp multilevel inverters, the introduced multilevel inverter produces more voltage levels using less components. The proposed inverter is used in photovoltaic (PV) system where the green power comes from two separate PV panels connected to the dc links through dc-dc converters to draw the maximum power. Due to boost operation of this inverter, two different PV panels can combine and send their powers to the grid. Simulations and experimental tests are conducted to investigate the good dynamic performance of the inverter in grid-connected PV 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations176
Published2018
Admission routes1
Has abstractyes

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