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Analysis of Symmetric and Asymmetric CHB-MLI Using MC based SPWM and THI-PWM

2020· article· en· W3127284837 on OpenAlexaff
Jigneshkumar Patel, Vijay K. Sood

Bibliographic record

Venue2020 IEEE Electric Power and Energy Conference (EPEC) · 2020
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPulse-width modulationHarmonicsHarmonic analysisControl theory (sociology)Topology (electrical circuits)VoltageComputer scienceElectronic engineeringEngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Cascaded multilevel inverter (CHB-MLI) has the advantages of modularity and flexibility to generate any number of output voltage levels. It can be configured as a symmetrical or asymmetrical type. The CHB-MLI can be controlled with various modulation techniques, like multi-carrier PWM (MC-PWM), selective harmonics elimination PWM (SHE-PWM), and space vector PWM (SV-PWM). Among these PWM methods, MC-PWM is very well accepted for CHB-MLI topology. This paper investigates, using Matlab simulation, the performance of symmetric and asymmetric CHB-MLI incorporated with MCPWM techniques, such as phase-shifted PWM (PS-PWM) and level-shifted PWM (LS-PWM). All MC-PWM methods are constructed using sinusoidal and third harmonic injected reference signals.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
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 head0.017
GPT teacher head0.204
Teacher spread0.186 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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