Influence of Grading Capacitance on Sympathetic Inrush Current of Parallel Power Transformers
Bibliographic record
Abstract
This article presents research on the phenomenon of sympathetic inrush current triggered by the energization of a third transformer in parallel with two others already connected, considering the impact of circuit-breaker grading capacitance, which is an emerging in the literature on the subject. The aim is to propose recommendations to mitigate the intensity of this phenomenon to reduce the risk of outages or system failures during transformer energization. The study focuses on a common configuration in the central Mexican electrical grid, which may include power transformers connected in parallel, which can be energized through their 230 or 85 kV windings. The research employs electrical power system modeling using the Alternative Transients Program software to simulate typical scenarios involving substation switch operations during grid activity. It incorporates transformer models with manufacturer-specified saturation characteristics and a certain level of remanent magnetization. With the results obtained, it is inferred that the magnitude, waveform, and duration of the sympathetic inrush currents can cause imbalances and affect the normal operation of the system. Transformer outages can occur due to malfunctioning of its differential and overcurrent relays, as well as power quality problems. Remanent magnetization is not a determining factor for the appearance of the phenomenon. However, the magnitude of the sympathetic inrush current is strongly related to the closing time of the circuit breaker. Furthermore, the findings indicate that a higher capacitance makes the sympathetic inrush current phenomenon more evident.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".