Comparison of Different Voltage Waveforms for Partial Discharge Measurement in Medium Voltage Cables and Accessories
Bibliographic record
Abstract
Partial discharge (PD) measurement is commonly used to identify weak spots in medium voltage (MV) cables and accessories, as an acceptance and a maintenance test procedure. A PD event leads to the continued accelerated aging of a cable or an accessory and does not heal itself. In this background, PD testing can spot localized issues in the cable insulation such as voids, air gaps and electrical trees, and workmanship problems at the cable accessories (due to misalignment, improper cutbacks, etc.). PD is a frequency-dependent phenomenon and is influenced by the rate of rise of the test voltage, especially at the point of zero-crossing. It is therefore important to analyze if the nature of the voltage waveform used has a significant effect on the PD measurement test results. This paper aims to evaluate the performance of different voltage waveforms, such as the power frequency (sinusoidal AC – 60 Hz), damped alternating current (DAC), and sinusoidal very low frequency (VLF) (at 0.1 Hz) for PD measurement in MV cables and accessories. The paper will demonstrate the sensitivity of the aforementioned offline PD measurement technologies in identifying defects in MV cable samples through a laboratory investigation when various voltage waveforms are applied. The PD measurement sensitivity in terms of the background noise level, localization capability as well as the identification of the partial discharge inception voltage (PDIV) and partial discharge extinction voltage (PDEV) values will also be highlighted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".