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Record W3005845943 · doi:10.1109/tpwrd.2020.2973407

Topological Transformer Leakage Modeling With Losses

2020· article· en· W3005845943 on OpenAlexaff
Mathieu Lambert, Manuel Martinez-Duro, Afshin Rezaei‐Zare, Jean Mahseredjian

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsPolytechnique MontréalYork UniversityHydro-Québec
Fundersnot available
KeywordsTransformerElectromagnetic coilTopology (electrical circuits)Leakage (economics)Electronic engineeringTest dataDistribution transformerEngineeringExperimental dataComputer scienceElectrical engineeringMathematicsVoltage

Abstract

fetched live from OpenAlex

This paper presents a novel leakage model for transformers that includes losses. This new model is physically based (topological) and it is possible to connect it to a topological core model without using any fictitious windings. The methods to calculate its parameters from standard test data and from design data are detailed within the paper. The results show that the new coupled leakage model with losses is able to match exactly the test report, when using standard test data. Preliminary investigations show that the results are very close to measurements, when using design data.

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 score0.576
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)0.0040.000

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 head0.030
GPT teacher head0.218
Teacher spread0.188 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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