A Review of Partial Discharge in Medium Voltage SiC Power Modules Under Square Wave Excitation: Characterization, Mitigation, and Detection
Bibliographic record
Abstract
The medium voltage (MV) silicon carbide (SiC) power modules hold significant promise as a route for the solid-state converters to achieve higher power density, higher switching frequency, and lower loss. However, the emergence of partial discharge (PD) in the MV SiC power modules has posed a formidable challenge to enhancing its nominal voltage. In this paper, a comprehensive review and comparative analysis of characterization, mitigation, and detection of PD under the square wave excitation is proposed for MV SiC power modules. First, the mechanism, detriments, and characterization of the PD issue and electrical tree in the MV power module are insightfully demonstrated. Meanwhile, the challenges from the PD for the MV SiC power module are emphasized. Besides, the geometric modification of direct bonded copper (DBC) structures to mitigate the PD issue are reviewed in detail. The limitations of the customized DBC are analyzed, considering the cost, reliability, and thermal resistance. Additionally, advanced dielectric materials are introduced and categorized. It is indicated that the effectiveness of the new dielectric under the square wave excitation stress still needs further investigation. Meanwhile, the optical, electromagnetic, electrical, and ultrasound detection methods under square wave excitation are further reviewed to assess the PD behavior in the MV SiC power module. Lastly, suggested future researches for MV SiC power module packaging are proposed, aiming to inspire new ideas to further promote the insulation level of the MV SiC power module.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".