Superimposed Energy-based Fault Detection and Classification Scheme for Series-compensated Line
Bibliographic record
Abstract
This article presents a fault detection and classification scheme for a series-compensated transmission line that is based on post-fault superimposed energy. The derivations of the scheme presented in this article are obtained with consideration of both the real and reactive components of the power system. However, the criterion depends only on the real components of the power system. For a forward fault, superimposed energy is negative, whereas it is positive for a reverse fault. If the relays of both ends detect forward fault, it is an internal fault; else, it is an external fault. The magnitude of superimposed energy depends on the fault type, location, and resistance, which makes it difficult to classify the type of fault as a fixed threshold cannot be set. Therefore, to classify the type of fault, energy coefficients have been introduced that depend on the superimposed energy measured at the relay. To test the capability of the superimposed energy based scheme, the test system has been simulated in PSCAD/EMTDC (Manitoba HVDC Research Centre) and an algorithm has been implemented in MATLAB (The MathWorks, Natick, Massachusetts, USA). Results proved that the scheme is accurate and robust against different system conditions and uncertainties.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".