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Record W2964961773 · doi:10.1109/gpecom.2019.8778518

A Minimum Number of Switch Five-Level Inverter for Renewable Energy Sources Applications

2019· article· en· W2964961773 on OpenAlexaff
Amirhosein Akbari, Fereshteh Poloei, Alireza Bakhshai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsInverterTotal harmonic distortionGrid-tie inverterComputer scienceVoltagePower (physics)Topology (electrical circuits)Electronic engineeringMATLABHarmonicElectrical engineeringMaximum power point trackingEngineeringPhysics

Abstract

fetched live from OpenAlex

A novel structure for a five-level voltage source inverter has been proposed in this article. This topology includes two symmetric power supplies connected in series to produce five levels in output. The main goals of presenting this structure are reduction of number of switches, complexity of control circuit, and Total Harmonic Distortion of the inverter output voltage (THDv) with the proper use of power semiconductor switches. In comparison with typical structures, the proposed inverter has lower number of switches. To verify the performance of the inverter, several simulations have been done by using MATLAB/Simulink software, and also a 3kVA laboratory prototype of the inverter has been implemented. Our proposed inverter has been compared with the presented former ones in terms of the number of power switches, total components, and power losses. Based on the simulation and experimental results, all mentioned factors have been improved.

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: Simulation or modeling · 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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.015
GPT teacher head0.220
Teacher spread0.206 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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