Online Partial Discharge Measurements in an Operating Generator Stator Winding Using UHF Antennas
Bibliographic record
Abstract
On stator windings, it is typical to perform online partial discharge (PD) measurements in the VHF range using high voltage capacitors to couple high frequency currents, and in the UHF range using near-field sensors placed adjacent to stator slot wedges to couple induced PD currents. The installation of conventional stator winding PD measurement systems can be challenging to utilities because it requires the generator to be out of service, and it also requires specialized labour to carefully install the sensors and associated wiring without damaging the asset. It would be advantageous to use antennas for online PD measurements since an outage may not be required and installation would be simple. This paper investigates antenna-based UHF techniques for online PD measurement, using rectangular microstrip patch antennas designed and fabricated with resonant frequencies of 900, 1500, and 2450 MHz. These antennas are used to detect and quantify PD on single Roebel bars in a laboratory and on the stator winding of an operating hydrogenerator. This is done by placing each antenna near the specimen under test, acquiring pulses with a digital oscilloscope, and generating PRPD patterns and time-frequency plots. Agreement with test results obtained with a commercial instrument shows that PD can be successfully measured with UHF antennas for every specimen tested.
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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 | 0.000 | 0.001 |
| 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 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".