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Record W1588353497 · doi:10.1109/ceidp.2005.1560674

Application of dielectric response techniques for the condition assessment of power transformers

2005· article· en· W1588353497 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsÉcole de Technologie SupérieureHydro-Québec
Fundersnot available
KeywordsDielectricDissipation factorAccelerated agingTransformerMaterials scienceTransformer oilDielectric responseElectronic engineeringMoistureDielectric lossElectrical engineeringComposite materialOptoelectronicsEngineeringVoltage

Abstract

fetched live from OpenAlex

In recent years, new diagnosis and monitoring techniques using the dielectric response have been developed for insulation condition assessment. Their sensitivities to aging and their potential to predict residual live is still to demonstrate. To respond to these issues, we have started an investigation and looked at the potential of two electrical methods to be sensitive to aging and how their signatures can be related to the transformer age and condition. This study covers the evaluation of the dissipation factor (tan /spl delta/) versus frequency and the polarization-depolarization current (PDC) measurements for their sensitivity to aging of the insulation. This evaluation is performed using model transformers with various moisture content and aging degrees. Insulation has been assessed at every stage of aging with oil and paper analysis. The two dielectric methods have been measured at every stage of aging. The results and the correlation between the dielectric measurements and the paper and oil aging parameters as well as the insulation moisture content are discussed.

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 categoriesnone
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 score0.958
Threshold uncertainty score0.165

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)0.0000.000

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 head0.006
GPT teacher head0.266
Teacher spread0.260 · 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