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Record W2119314139 · doi:10.1109/tie.2010.2081957

Analysis and Development of Wavelet Modulation for Three-Phase Voltage-Source Inverters

2010· article· en· W2119314139 on OpenAlexaff
S. A. Saleh, Cecilia Moloney, M. Azizur Rahman

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2010
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWaveletInverterPulse-width modulationModulation (music)Sampling (signal processing)Basis functionBasis (linear algebra)SIGNAL (programming language)Electronic engineeringHarmonicVoltageComputer scienceControl theory (sociology)MathematicsPhysicsEngineeringAcousticsTelecommunicationsElectrical engineeringArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

This paper presents the analysis, development, and implementation of a wavelet modulation (WM) technique for three-phase voltage-source (VS) six-pulse inverters. The WM technique is based on sampling three continuous-time (CT) sinusoidal reference modulating signals in a nonuniform recurrent manner using three sets of scale-based linearly combined wavelet basis functions. These CT signals are recovered from their samples by a three-phase VS six-pulse inverter, which is activated by three sets of synthesis scale-based linearly combined wavelet basis functions. Each set of synthesis basis functions is generated for activating one leg of the three-phase inverter in order to recover one CT reference modulating signal. The WM technique is implemented for both simulation and experimental performance testing. The performances of the WM technique are compared with those obtained using the space vector modulation, random pulsewidth modulation, and hysteresis band current control techniques under the same loading conditions. Simulation and experimental test results show that the proposed WM technique is able to switch a three-phase VS six-pulse inverter to produce outputs with significantly improved fundamental components and low harmonic contents.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.027
GPT teacher head0.243
Teacher spread0.215 · 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.

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

Citations70
Published2010
Admission routes1
Has abstractyes

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