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Record W2289813183 · doi:10.1109/iemdc.2015.7409142

Modeling and control of a hysteresis IPM motor drive for electric submersible pumps

2015· article· en· W2289813183 on OpenAlexaff
S. F. Rabbi, P.G. Brown, N. Drover, Christopher Kennedy, M.A. Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsNewfoundland and Labrador Centre for Applied Health Research
Fundersnot available
KeywordsInduction motorHysteresisElectric motorTorqueEngineeringController (irrigation)AC motorControl theory (sociology)Automotive engineeringSynchronous motorControl engineeringComputer scienceVoltageMechanical engineeringControl (management)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents the modeling and performance analysis of a hysteresis IPM motor drive for electric submersible pumps (ESPs). The purpose of this paper is to extend the run-life of the downhole ESPs in offshore oil recovery plants by introducing a more reliable and more efficient submersible hysteresis IPM motor drive into the existing ESP technology. An adjustable speed controller has been designed in order to soft-start the motor at variable frequencies. Simulations have been carried out to obtain the performances of a 3-phase 4-pole 208V 2.5 kW hysteresis IPM motor drive for ESP load. Experimental investigations have been also carried out for a laboratory prototype 2.5 kW hysteresis IPM motor. Both the simulation and experimental results are presented and analyzed in this paper. Based on the simulation and the experimental results, the hysteresis IPM motor can be a possible replacement for the standard induction motors currently used for downhole ESPs.

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)0.000
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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