Numerical Modeling Method of Turn-to-Turn Transient Voltages Taking Into Account the Common Mode Interactions
Bibliographic record
Abstract
Electrifying future aircraft requires the development of advanced embedded electrical power systems. To achieve this objective, increasing on-board voltage levels are needed, beyond the conventional reference values of 230VAC/540VDC. In More-Electric Aircraft (MEA) and All-Electric Aircraft (AEA) applications, rotating electrical machines powered by PulseWidth Modulation (PWM) inverters are widely used. However, the use of these fast-switching PWM inverters combined with the presence of non-negligible cable lengths leads to significant overvoltages at the machine terminals (caused by waveform reflection phenomena) and non-uniform voltage distribution within stator windings. This leads to an increased risk of Partial Discharge (PD) inception, as turn-to-turn voltages may exceed the Partial Discharge Inception Voltage (PDIV). The risk is further exacerbated by the use of Wide Band Gap (WBG) inverters, which enable higher switching frequencies and faster voltage rise times. Combined with the harsh environmental conditions associated to high-altitude operations, these factors further increase the probability of PD inception and insulation breakdown in machine windings. This paper presents a time-domain validation of a High-Frequency (HF) model of an electrical machine stator windings. The method uses a two-time-scale approach, accounting for the electrical machine’s operating environment to predict turn-to-turn and turn-to-ground voltage distributions. In order to take into account the HF behavior of the different elements constituting the stator winding, an experimental validation is carried out using three systems of increasing complexity: an elementary air-core coil, an intermediate ferrite core coil, and a more representative coil with a laminated-iron core to simulate the materials of electrical machines.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".