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Record W4285157842 · doi:10.1109/tpwrd.2022.3177137

Electromagnetic Modeling of Transformers in EMT-Type Software by a Circuit-Based Method

2022· article· en· W4285157842 on OpenAlexaff
Sadegh Rahimi Pordanjani, Mohammed Naïdjate, Nicolas Bracikowski, Mircea Fratila, Jean Mahseredjian, Afshin Rezaei‐Zare

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsYork UniversityPolytechnique Montréal
Fundersnot available
KeywordsTransformerFinite element methodSoftwareElectronic engineeringCurrent transformerMagnetic coreMagnetic circuitComputer scienceMagnetic fluxEngineeringElectrical engineeringElectromagnetic coilVoltageMagnetic fieldPhysicsStructural engineering

Abstract

fetched live from OpenAlex

This work proposes a fully circuit-based method for modelling electrical transformers. This method not only offers the advantages of circuit-based methods and can be implemented in electromagnetic transient (EMT) type software, but it can also provide a detailed representation of transformers, comparable to the finite element method (FEM). The proposed method enables a detailed geometrical modelling, as well as representation of magnetic flux paths and consideration of iron core saturation. It can be implemented in EMT-type software to see the effect of power networks on transformers. In addition, the proposed method can represent internal faults in transformers. The problem is constrained to a 2-D domain, which is often used in FEMs to represent the magnetic behavior of power equipment. Finite element analysis based on ANSYS Maxwell is used to verify the proposed method.

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 categoriesInsufficient payload (model declined to judge)
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 score0.634
Threshold uncertainty score0.997

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)0.0030.000

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 head0.233
Teacher spread0.217 · 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.

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

Citations9
Published2022
Admission routes1
Has abstractyes

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