Effect of Repetitive Impulse Waveform Characteristics on Partial Discharges in Type II Turn-to-Turn Insulation
Bibliographic record
Abstract
In this article, the effect of repetitive square wave impulse voltage, switching frequency, rise time, and overshoot (OS), on partial discharges (PDs) within Type II turn-to-turn insulation is reported. Two types of samples are used in this study: a back-to-back turn insulation sample and a single cavity layered insulation. This work aims to analyze the influence of the square wave impulse voltage parameters on the characteristics of PDs during aging while relating their effect on the life of turn insulation. The experimental setup used in this study separates the effect of the waveform rise time from the OS and highlights the role of the OS rather than the jump voltage in aging. The results, within the range of parameters considered in the study, indicate that the number and magnitude of PDs are lower at a higher switching frequency while shorter rise time triggers fewer discharges of larger magnitudes. It is also found that PDs, both occurrences and magnitudes, are in favor of waveforms of higher OSs even when the waveforms have a similar jump voltage. This highlights the role of the OS, rather than the jump voltage, as a main and additional factor affecting the life of turn insulation which is contrary to what has been described in the International Electrotechnical Commission (IEC) 60034-18-42. Based on the average magnitude of PDs per unit time, the results show, in the reported range of parameters, that the switching frequency and OS of the voltage waveform are among the main parameters affecting turn insulation life, whereas the effect of the rise time is found to be minimal.
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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".