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Record W2020464083 · doi:10.1109/epe.2007.4417232

Real-time performance testing of a 3Φ VS WM inverter-fed induction motor

2007· article· en· W2020464083 on OpenAlexaff
S. A. Saleh, Mohammad Azizur Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInverterInduction motorInsulated-gate bipolar transistorElectronic engineeringPulse-width modulationWaveformComputer scienceControl theory (sociology)VoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a real-time performance testing of a three-phase (3phi) voltage-source (VS) six-pulse wavelet-modulated (WM) inverter-fed induction motor (IM). The proposed modulation technique is based on a set of three scale-based linearly-combined scaling functions shifted by 2pideg/3 from each other. These scaling functions are employed to sample three reference-modulating signals, each corresponds to one phase, in a non-uniform recurrent manner. Moreover, these scaling functions have dual synthesis scaling functions that are responsible for reconstructing the three continuous-time (CT) reference- modulating signals from their non-uniform recurrent samples. The reconstruction processes are carried out by the three synthesis scaling functions to achieve 180-degrees conduction mode of the 3phi six-pulse inverter. The proposed wavelet modulation technique is realized through a Turbo - C code that is executed by a dSPACE dsl 102 DSP board to generate switching pulses to activate the inverter six IGBT switches. The IGBT inverter supplies a 1 hp, 1750 RPM, 208 V, 60 Hz, F-connected 3phi squirrel-cage induction motor that is tested for several speeds. These test results demonstrate robust performance, simple implementation, significant dynamic responses and high ability to maintain high quality outputs.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.665

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 head0.026
GPT teacher head0.220
Teacher spread0.194 · 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 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

Citations5
Published2007
Admission routes1
Has abstractyes

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