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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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.824

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 head0.031
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 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
Published2022
Admission routes1
Has abstractyes

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