MétaCan
Menu
Back to cohort
Record W2102273020 · doi:10.1109/tpwrd.2004.834891

Modeling and Protection of a Three-Phase Power Transformer Using Wavelet Packet Transform

2005· article· en· W2102273020 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2005
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWavelet packet decompositionInrush currentWavelet transformWaveletDiscrete wavelet transformSecond-generation wavelet transformStationary wavelet transformTransformerElectronic engineeringAlgorithmComputer scienceEngineeringElectrical engineeringArtificial intelligenceVoltage

Abstract

fetched live from OpenAlex

This paper introduces a novel algorithm for differential protection of three-phase power transformers that is based on the wavelet packet transform. The wavelet packet transform is employed to extract certain features of the differential current to distinguish between the magnetizing inrush and different internal fault currents. The selection of the optimal wavelet analysis that includes selecting both the optimal mother wavelet and the optimal number of levels of resolution is carried out using the minimum description length (MDL) data criteria. The proposed algorithm is tested off-line using data collected from a prototype laboratory three-phase power transformer. The test results show reduced computational burden, high speed and high accuracy.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.569
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.021
GPT teacher head0.238
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