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Record W2135424655 · doi:10.1109/tia.2010.2046293

Testing of a Wavelet-Packet-Transform-Based Differential Protection for Resistance-Grounded Three-Phase Transformers

2010· article· en· W2135424655 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 Industry Applications · 2010
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInrush currentCurrent transformerEngineeringElectronic engineeringTransformerDistribution transformerWaveletWavelet packet decompositionWavelet transformControl theory (sociology)Computer scienceElectrical engineeringVoltageArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents an extension of real-time tests of a wavelet-packet-transform-based technique for the differential protection of three-phase power transformers. The proposed technique is implemented using a DS1102 digital signal processor board and tested on two different three-phase power transformers with neutral resistance grounded. Different magnetizing inrush and internal fault currents are investigated with current transformer (CT) saturation for different loading conditions, including capacitive loads. The results show a complete independence from transformer parameters, load types, grounding method, or CT saturation. Furthermore, the proposed technique has high speed, good accuracy, small required memory, and reliable responses with reduced computational burden. In all cases of the investigated internal faults, the proposed algorithm is capable of identifying the fault and generating a trip signal in less than a quarter cycle based on a 60-Hz system.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.026
GPT teacher head0.267
Teacher spread0.241 · 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