Simulation of Switching Overvoltages and Validation With Field Tests
Bibliographic record
Abstract
On transmission lines where switching surges are not mitigated with closing resistors and/or surge arresters, high-speed reclosing on a line with trapped charge will produce high overvoltages that have been measured above 3 p.u. Careful simulations of these switching events using available electromagnetic transient (EMT) programs consistently produce significantly higher voltages than the measurements. This indicates a phenomenon present on the line that acts to reduce the switching surge magnitudes but is not typically modeled. This paper aims to identify the required simulation practices in reproducing the field measured overvoltages in EMT simulations and investigates the sensitivity of results to modeling approaches and electrical parameters. Field measurements from a switching surge test of a 230-kV line without surge mitigation have been used for model comparisons. Variations in frequency-dependent line modeling, ground resistivity, skin effect, shunt conductance, parallel lines, and source-side detail have been tested in an unsuccessful attempt to decrease the difference between the field measurements and the higher simulation overvoltages. It is demonstrated that even though the pattern of the transient voltage waveforms can be reproduced very well using frequency-dependent line models, the magnitude of the maximum overvoltage is significantly overestimated unless the effect of corona is considered. Two types of corona models are tested, and both demonstrate that corona is the primary factor that allows the simulations to correctly reproduce high peak overvoltage measurements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".