MétaCan
Menu
Back to cohort
Record W4200521082 · doi:10.1002/2050-7038.13209

Performance evaluation of a grid‐connected three‐phase six‐switch boost‐type current source <scp>PV</scp> inverter under different switching strategies

2021· article· en· W4200521082 on OpenAlexaff
Kawther Ezzeddine, Mahmoud Hamouda, M.A. Belaïd, Hadi Y. Kanaan, Kamal Al‐Haddad

Bibliographic record

VenueInternational Transactions on Electrical Energy Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsRipplePhotovoltaic systemReliability (semiconductor)Power (physics)Computer scienceGrid connectionElectrical engineeringInverterElectronic engineeringVoltageBoost converterEngineeringPhysics

Abstract

fetched live from OpenAlex

Recently, boost-type current source inverters (CSIs) have received a considerable attention in grid connected photovoltaic (PV) applications thanks to their salient features such as high reliability and the aptitude to operate with a DC-link voltage lower than the grid side voltage. This paper investigates the performance of three different switching strategies for grid-connected six-switch boost-type CSI employed in a solar power conversion system. These strategies are analyzed in terms of the global average conduction and switching power losses, DC-link current ripple, and AC power quality. The proposed evaluation is based on experimental investigations of the power devices behavior and computer co-simulations. The obtained numerical and experimental results prove that the switching strategy with a freewheeling state placed in the middle of the overall pulse-period is the most appropriate in terms of efficiency and power quality.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.266
Teacher spread0.235 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Transactions on Electrical Energy SystemsSame topicMultilevel Inverters and ConvertersFrench-language works237,207