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Record W2119742377 · doi:10.1109/tpwrd.2002.803717

No-load losses in transformer under overexcitation/inrush-current conditions: tests and a new model

2002· article· en· W2119742377 on OpenAlexaff
A. Gaudreau, Patrick Picher, L. Bolduc, André Coutu

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2002
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsInrush currentTransformerHarmonicsEmtpControl theory (sociology)EngineeringMechanicsVoltagePhysicsElectrical engineeringElectric power systemComputer sciencePower (physics)

Abstract

fetched live from OpenAlex

Tests were conducted on several transformers rated at 100 kVA or less and on a power transformer rated 370 MVA in an effort to characterize the no-load losses and magnetizing resistance for transformers subjected to overexcitation and inrush current. Analysis of the results revealed that the magnetizing resistance changes as a function of the peak magnetization flux or the amplitude of the magnetic field. A new model of the instantaneous magnetizing resistance (IMR) as a function of the instantaneous flux has been developed and its dynamic use in the Electromagnetic Transients Program (EMTP) allows the form of the hysteresis cycle and the mean losses in overexcitation to be reproduced with a high degree of accuracy. The same model also accounts for losses due to the harmonics superimposed on the fundamental. The results showed that the IMR calculated under inrush current conditions is higher than that in overexcitation conditions.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.259
Teacher spread0.218 · 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

Citations27
Published2002
Admission routes1
Has abstractyes

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