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Record W2603600387 · doi:10.1109/jestpe.2017.2688726

A Selective Harmonic Elimination Method for Five-Level Converters for Distributed Generation

2017· article· en· W2603600387 on OpenAlexaff
Concettina Buccella, Carlo Cecati, Maria Gabriella Cimoroni, Gnana Sambandam Kulothungan, Amarendra Edpuganti, Akshay Kumar Rathore

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsConvertersModulation indexHarmonicHarmonic analysisElectronic engineeringControl theory (sociology)Computer sciencePower (physics)VoltageModulation (music)Distributed generationEngineeringPulse-width modulationControl (management)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

In distributed generation, high power capability and high efficiency are very important requirements. For this reason, this paper proposes a new analytical procedure for selective harmonic elimination in five-level inverters operating at fundamental frequency. For each modulation index, the proposed method calculates all possible switching angles eliminating a specified odd harmonic from the output voltage. The method has limited computational complexity, simple and cost-effective real-time implementation, and full capability of integration with closed loop control. Simulation and experimental results highlight full elimination of selected harmonic.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.298
Teacher spread0.268 · 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

Citations95
Published2017
Admission routes1
Has abstractyes

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