Experience with Dielectric Dissipation Factor Testing of Complete Stator Windings
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".