Initial Experience with Acoustic Imaging of PD on High Voltage Equipment
Bibliographic record
Abstract
Directional ultrasonic microphones have been used for decades to locate surface partial discharge and corona sites in high voltage equipment. However, there was always some uncertainty of exactly where the discharge sites were due to reflections and the field of view of the detector. In addition, scanning a complete winding took some time. Recently, a significant advance in this technology was achieved with the commercial development of an acoustic “camera” that can show where the sound is occurring with respect to a normal visible-light image of the test object. That is, the device produces a sound image of the PD on the test object, much like a UV camera locates the ultraviolet light from PD on an image of the test object. The acoustic camera takes advantage of a large array of wideband microphones and is able to display the acoustic signal in selectable frequency ranges in the sonic and ultrasonic ranges. The effectiveness of this new tool was evaluated on a point to plane corona test object, as well as stator coils and stators with known PD. The optimum surface discharge detection band seems to be from 30–50 kHz: Intense PD in internal voids may also be detected, albeit at a lower frequency than for surface PD. Precise locations of multiple discharge sites are rapidly identified in different test objects.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".