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Record W2940055182 · doi:10.1109/ecai.2018.8678996

Space Vector PWM-DTC in an application for crude oil extraction in Canadian version

2018· article· en· W2940055182 on OpenAlexaboutno aff
Boris Siro, Cornel Ianache, Alexandru Săvulescu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsnot available
Fundersnot available
KeywordsTorqueInduction motorDecoupling (probability)Pulse-width modulationComputer scienceControl theory (sociology)VoltageEngineeringControl engineeringElectrical engineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The paper presents a study of the behavior of an induction motor drive with Space Vector PWM-DTC algorithm, in a simulation for a crude oil extraction application, which requires robust torque sets, specific to complete extraction strokes, on a wide range of operating speed variations. The SV-DTC variant drops the hysteresis regulators of classical DTC technique in favor of two PI regulators, thus managing a better decoupling of the mechanical phenomena from the magnetic ones of the machine, which will be highlighted by the results obtained from the simulations. It has been proposed as novelty an extension of the simulation scheme to obtain the values of the energy losses of the electric drive, losses that could be recovered by the owner of the field oil equipment, because on certain portions the electric machine works in the generator mode.

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 score0.785
Threshold uncertainty score0.873

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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