Automated Impulse PD Testing for Early Detection and Classification of PD in the Stator Insulation of Low Voltage VFD Motors
Bibliographic record
Abstract
Electrical stresses in industrial variable frequency drive (VFD) motors are increasing with the increasing voltage and dv/dt levels, and with the advent of wide bandgap power devices. This increases the likelihood of partial discharge (PD) in VFD motors, and is expected to increase the risk of stator insulation failures in low voltage (LV) motors for which the insulation is not resistant to PD. To ensure that PD does not occur during operation, LV VFD motors are qualified for PD-free operation in the design or manufacturing stages. However, the PD inception voltage of qualified LV motors decreases with insulation aging exposing them to the risk of PD-induced failure. In this work, an automated off-line test method for VFD-embedded PD testing is proposed. The main idea is to perform PD testing at motor standstill at a voltage higher than the operating voltage to identify PD activity in the insulation early, before it occurs during operation. A series of impulse voltage tests are proposed for stressing the different components of insulation for identifying PD in terminal-end ground, phase, and turn insulation. Testing on a LV, VFD motor verifies that PD can be identified whenever the motor is stopped to provide early warning of insulation failure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".