Supplementary Impedance-Based Fault-Location Algorithm for Series-Compensated Lines
Bibliographic record
Abstract
This paper presents a new impedance-based supplementary fault-location algorithm for series capacitor-compensated transmission lines (SCCTLs), which improves the accuracy of the existing fault-location algorithms. The proposed algorithm utilizes the fact that the metal-oxide varistor (MOV) may become bypassed in faulted or all phases before the interruption of fault for certain fault scenarios. The removal of the nonlinear element, that is, MOV from the fault loop enables the proposed algorithm to provide more accurate fault-location results compared to the most advanced impedance-based technique. Another major advantage of the proposed algorithm is that the dedicated subroutines are not required for the location of a fault in a particular section of the transmission line. The proposed fault-location algorithm is rigorously tested for various fault scenarios in the 500-kV SCCTL simulated in PSCAD. The performance of the proposed algorithm is compared to a well-known existing impedance-based fault-location algorithm for SCCTLs to illustrate higher accuracy and improved sensitivity of the proposed technique.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".