Impact of Surge Capacitor-SF6 Breaker Separation on Thermal Interrupting Capability
Bibliographic record
Abstract
Test standards/application guidelines on breaker transient recovery voltage (TRV) assume line-side TRV is delayed by 0.5 μs by station capacitance during short-line fault (SLF) clearing on 230-kV lines. This assumption can be critical for SF6 breakers having an arc time constant comparable to the assumed delay. To delay the line-side TRV sufficiently, a 230-kV SF6 breaker may require surge capacitors at its terminals during SLF type tests. However, the surge capacitor installation in the field may only be feasible at some distances away from the breaker due to insufficient clearance at its terminals. The separation between the breaker and its required capacitor can lead to an initial TRV (ITRV) much higher than the type-tested duty. This paper investigates the consequence of this separation. Firstly, a set of TRV data collected from a 245-kV interrupter's type tests are used to calibrate Cassie-Mayr arc model parameters. Afterwards, the calibrated arc model is employed to investigate the surge capacitor-breaker separation effect on thermal interrupting capability under SLF-caused ITRVs. The studies reveal as the separation between the breaker and capacitor increases, the arc model's interrupting capability decreases significantly, suggesting there may be merit in updating the TRV test standards/application guidelines to address this phenomenon.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".