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Experience with Dielectric Dissipation Factor Testing of Complete Stator Windings

2024· article· en· W4400351418 on OpenAlexaff
Ashfak Shaikh, H.G. Sedding

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsKinectrics (Canada)
Fundersnot available
KeywordsStatorDissipationElectromagnetic coilDissipation factorDielectricElectrical engineeringMaterials scienceElectronic engineeringPhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Dielectric dissipation factor (DDF) testing has been widely employed for many decades by OEMs, end users and service providers as a tool to assess stator winding insulation condition. The test method is governed by international standards, IEEE 286 [1] and IEC 60034-27-3 [2], with the latter standard providing acceptance limits for new stator bars and coils. However, IEC 60034-27-3 specifically precludes the use of these limits on completely assembled stator windings due to the presence of the stress grading materials employed on high voltage rotating machines. Measurement of dissipation factor and tip-up is complicated by the presence of silicon carbide stress control coatings on coils or bars rated at 6 kV or above. This coating creates a noise floor that interferes with the measurement. Very significant partial discharge (PD) must be occurring in most windings for the PD loss to be seen above the silicon carbide tip-up. The influence of the losses on DDF measurements associated with the stress grading materials may be significantly reduced using guard electrodes as described in the standards. However, guard methods are not practical on complete stator windings.Despite this limitation, many organizations routinely employ DDF testing on complete stator windings (or individual phases) often in conjunction with other dielectric tests such as insulation resistance, partial discharge, high voltage dc ramp, etc. Given the lack of any acceptance criteria for DDF test results, the data is trended and/or the results from individual phases are compared to determine if any anomalies are present that may indicate degraded insulation condition. Some organizations have empirically derived internal values for what is considered acceptable DDF on complete stator windings. Dielectric dissipation factor data may also be compared to PD measurements, if made, because both tests provide a measure of void content.This contribution, based on DDF data obtained over many years on a number of service-aged stator windings, will examine the effectiveness of trending the data as well as determining the prospects for applying any DDF limits, e.g., absolute DDF or tip-up values, to aid condition assessment of the stator winding insulation 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.

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 score0.056
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 communication0.0000.000
Open science0.0000.000
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 head0.044
GPT teacher head0.266
Teacher spread0.223 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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