A Comparison of PD Detection Techniques for Complete Assemblies of Air-Insulated Switchgear
Bibliographic record
Abstract
This publication presents a comparison between conventional PD measurements, TEV PD detection and ultrasonic PD detection applied on medium-voltage air-insulated switchgear. Several types of anomalies which generated PD were introduced in a 15 kV rated metal-clad distribution switchgear and in a 35 kV rated metal-enclosed distribution switchgear. PD measurements were then performed in a factory environment to verify the sensitivity of each technique using commercially available instruments. Additional measurements were performed by combining stand-alone TEV sensors with an advance PD instrument, which allows freely configurable digital filters to be applied to the acquired signals.It was found that ultrasonic detection using a camera is very useful at localizing external PD with a clear line of sight. The wideband handheld TEV instrument used in those experiments detected a variety of defects but was limited to PD sources that generated relatively high levels of apparent charge, as measured by conventional measurements. The combination of the advanced PD instrument with the stand-alone TEV sensors greatly improved the sensitivity of PD detection by allowing a configurable digital filter to be tailored according to the actual test environment. It is also important to mention that all defects were detected when using conventional PD measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".