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Record W4207083645 · doi:10.17762/de.vol2022iss1.8729

Control Strategy for 5∅ Dual-Stator Winding Induction Starter/Generator Scheme

2022· article· en· W4207083645 on OpenAlexvenueno aff
M. Anusha

Bibliographic record

VenueDesign Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStatorRotor (electric)AC powerShunt generatorGenerator (circuit theory)Electromagnetic coilControl theory (sociology)EngineeringTorquePower (physics)Induction generatorComputer scienceElectrical engineeringVoltageControl (management)Physics

Abstract

fetched live from OpenAlex

Thisstudypresentsasystematicregulationapproachforastarter/generator(S/G)technologydependingupon a (FPDWIM). The FPDWIM features two sets of stator windings and a cage-type rotor. The first was a 5∅ control winding (CW), while second is a 5∅ power winding (PW). The FPDWIM acts as a motor when it is turned on. The CONTROL WINDING drives the engine by providing both active and reactive power. The CONTROL WINDING handles reactive power in the producing phase, whereas the POWER WINDING delivers active power. To complete the combination of the beginning and producing control, ICWFOC is designed to perform in both starting and generating modes. The CONTROL WINDING current and flux are controlled in beginning mode to produce a consistent starting torque, while the CONTROL WINDING and POWER WINDING Direct Current bus voltages were controlled for producing mode. As a result, these methods' operational concepts and topologies remain equivalent, making implementation and performance easier. Using the suggested control method, this network may complete the starting-generating process having more better transitioning operation.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.205
Teacher spread0.184 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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