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Record W1895980072 · doi:10.1109/lescpe.2002.1020666

Transient model of power transformer using wavelet filter bank

2003· article· en· W1895980072 on OpenAlexaff
S. A. Saleh, M.A. Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInrush currentWaveletFilter bankElectronic engineeringTransformerDelta-wye transformerWavelet transformControl theory (sociology)Distribution transformerComputer scienceAcousticsEngineeringFilter (signal processing)PhysicsElectrical engineeringVoltageArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a model of the power transformer for electromagnetic transient studies, which employs a wavelet filter bank. In this model both the primary and the secondary windings are represented by stages of a wavelet filter bank where the dimensions of such a filter bank depend on the number of turns of primary windings and secondary windings. The basic building block consists of stages of the wavelet filter bank. Transformer total response is composed of the sum of responses of all stages, which depends on the nature of the primary current. Using the wavelet filter model, the losses due to eddy currents, leakage flux, and inrush currents have been studied. The proposed wavelet filter model has been found to be suitable for power transformer inrush currents including high frequency transient effects.

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: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.993

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)0.0080.000

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 head0.038
GPT teacher head0.240
Teacher spread0.202 · 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

Citations4
Published2003
Admission routes1
Has abstractyes

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