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Record W4206613136 · doi:10.1109/icjece.2021.3108134

A Novel Plug-In Core Design for Three-Phase Transformers Une nouvelle conception de noyau enfichable pour les transformateurs triphasés

2022· article· fr· W4206613136 on OpenAlexvenueno aff
Dogancan Celen, Sibel Zorlu Partal

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2022
Typearticle
Languagefr
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsInrush currentTransformerDistribution transformerEnergy efficient transformerElectrical engineeringStackingLinear variable differential transformerMagnetic coreDelta-wye transformerMaterials scienceEngineeringVoltagePhysicsElectromagnetic coilNuclear magnetic resonance

Abstract

fetched live from OpenAlex

In this study, in order to reduce stacking time and production costs of transformer electrical sheets, a new Plug-In core model has been proposed. This Plug-In transformer was designed as a three-phase transformer made with M330-50A electrical steel with a rated power of 4.7 kVA and then produced as a prototype. Both the proposed transformer model and a reference EI-core transformer with same rated power and electrical ratings were analyzed using an ANSYS Maxwell 2-D simulation program and the results were compared. The distribution of magnetic flux densities, core losses, the local regions where core losses mostly occur, copper losses, and transient inrush currents have been simulated for both the transformers. Apart from simulation, load and no-load tests have been tested and the efficiency analysis of the transformers was determined. It has been determined that considerably less time and labor are required for stacking and assembly progress of the proposed core compared to the reference EI core transformer.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.005

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.031
GPT teacher head0.227
Teacher spread0.196 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Electrical and Computer EngineeringSame topicMagnetic Properties and ApplicationsFrench-language works237,207