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Record W2056538603 · doi:10.1109/isie.2006.295967

A New Reflected Wave Modeling Technique for PWM ASD-Motors

2006· article· en· W2056538603 on OpenAlexaff
Saïd Amarir, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsEmtpMATLABOvervoltageInverterTransient (computer programming)Electrical impedanceVoltageAdjustable-speed drivePulse-width modulationComputer scienceElectronic engineeringEngineeringElectrical engineeringControl theory (sociology)PhysicsElectric power system

Abstract

fetched live from OpenAlex

This paper presents a new technique to model the high frequency overvoltages and their associated currents due to the inverter supply and the cable length on PWM ASD-motors. Detailed mathematical formulas describing the transient voltage and current in the cable are developed. Modeling takes into account: cable physical (length) and electrical characteristics (R,L,G,C; inverter impedance as well as voltage rise and fall times; motor impedance. The proposed technique for modeling overvoltages, applicable for ASDs with lossless cables, proves to be faster during execution and more precise in simulation than the techniques used in PSB and EMTP softwares. It is more adapted to the ASDs in coordination between the systems and their feeding cables in order to protect the motor. This paper includes simulation results using Matlab/Simulink softwares and important experimental outcomes on an industrial 5 kVA ASD prototype

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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
Published2006
Admission routes1
Has abstractyes

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