Neutral Reactor Structures for Improved Single Phase Auto Reclosing in Multi-Circuit Multi-Voltage Transmission Lines
Bibliographic record
Abstract
In this paper, novel neutral reactor structures are proposed to mitigate the secondary arc current (SAC) in non-conventional Multi-circuit Multi-voltage Transmission lines (MCMVTLs). The parameters of the reactors in the structure are optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The optimization algorithm, coded in Python, regulates the multiple runs of an Electromagnetic Transients (EMT) simulation program to minimize the secondary arc current. The optimization trials are run in parallel on a 64 core computer to minimize the solution time. A detailed arc model was tested on the EMT program (PSCAD). Using this model and optimized parameters in the proposed neutral reactor schemes, the SAC is indeed extinguished in acceptable times. The SAC and TRV for neutral reactors with optimized parameters are up-to 68 and 77%, respectively, lower than the ones with parameters calculated by the conventional approach which ignores inter-circuit coupling. Meanwhile, the extinction times are up-to 46% faster. As a result, single-phase auto-reclosing in MCMVTLs is viable.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".