Partial discharge detection and identification at low air pressure in noisy environment
Bibliographic record
Abstract
Abstract Increasing demand for electric power in more electric aircraft requires a higher operating voltage for the power system that leads to higher electric stress on the insulation system. Operating at high altitudes where the electrical strength of air is weakened exposes the insulation system to even higher levels of electrical stress. As such, the probability of partial discharges (PD), which result in insulation degradation and failure, is higher. The presence of switching circuits in the power distribution system of the more electric aircraft is capable of producing high levels of noise. This noise in combination with the background noise and parasitic impedances will cause high amplitude ringing in the measured partial discharge signal waveform. Here, a method based on the combination of the wavelet and energy techniques is employed to detect PD pulses in a noisy environment under the low air pressure condition. To verify the technique, a laboratory setup consisting of two separate PD sources mounted in low air pressure (33 kPa) chamber is developed where sine and square waveforms are used as the applied voltage. The obtained results demonstrate that the proposed approach offers low computational complexity, high performance in PD phase localization, and robustness in a noisy environment.
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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) | 0.000 | 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".