Dynamic interactive characteristics between icicle growth and corona discharge on HVDC outdoor insulators during icing accretion
Bibliographic record
Abstract
Abstract In order to improve the stability and reliability of high voltage direct current (HVDC) transmission lines in cold regions, this article investigated the corona discharges and the icicle growth characteristics on outdoor insulators under DC voltages. The experiments were conducted at CIGELE laboratory at University of Quebec. The simulation model of corona discharges and the theoretical model of icicle growth were established. The dynamic process and interactive characteristics between the icicle growth and corona discharges under different HVDC voltages were obtained. The results showed that the discharge activity was strong and the cooling effect of ionic air was weak under negative DC voltage, resulting in a large number of bubbles inside the icicle. More negative ions were produced under negative DC voltage, which enhanced the polarisation of water droplets, leading to a greater density of ice beads outside the icicles. In addition, the longest icicles of each insulator under high humidity could bridge the shed gap. The accumulated charge and leakage current were higher under negative DC voltage, as well as the corona discharge at the top of the icicles lasted longer. Therefore, the negative DC corona discharge more significantly inhibited the growth of the icicle, resulting in a slower growth rate of the icicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".