Simulation Analysis of Electric Field of Air Gap Defect in 10KV XLPE Cable Based on COMSOL
Bibliographic record
Abstract
With the continuous advancement of urbanization and the improvement of people's living standards, power cables have become an important part of the power system; cross-linked polyethylene insulated cables have good electrical insulation properties, high mechanical strength, and high carrying capacity. The advantages have been widely used in high-pressure and ultra-high-pressure fields. However, during the manufacturing and operation of cables, small air bubbles may occur in the XLPE, which can cause distortion in the distribution of electric fields in the cable.The present study used the COMSOL finite element software for simulation to emulate the electric field in XLPE cables, specifically investigating the distribution features of bubbles of various sizes and locations in the 10kV XLPE cables. The results of the simulation show that the presence of bubbles inside the XLPE layer leads to a significant decrease in potential at the interface among the bubble with XLPE. Additionally, the electrical field intensity at the position of the bubble is greater compared to the electric field strength in the absence of bubbles.And as the position of the bubble gets closer to the outer layer of XLPE, the maximum field strength at the bubble becomes smaller.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".