A Novel Hybrid Differential Algorithm for Turn to Turn Fault Detection in Shunt Reactors
Bibliographic record
Abstract
Turn to turn faults usually cause smaller changes in the phase currents of a shunt reactor when fewer turns are involved. Designing a sensitive and reliable algorithm for turn to turn fault detection in shunt reactors still remains a challenge. In this paper, a novel hybrid differential algorithm has been proposed to detect turn to turn faults in shunt reactors. The proposed algorithm calculates the difference between normalized negative sequence terminal voltage and normalized negative sequence reactor current phasors. This difference value is used for detecting turn to turn fault in shunt reactors. The proposed algorithm can also identify the faulty phase. This is a significant improvement with respect to the existing negative or zero sequence based methods. The proposed algorithm does not need neural CT. Impedance values of the shunt reactors are also not needed in the calculations. The proposed algorithm can be applied to both solidly and impedance grounded shunt reactors. The performance of the proposed algorithm is evaluated using PSCAD simulations. It is found that the proposed algorithm is sensitive enough to detect lower level turn to turn faults. The proposed algorithm performs satisfactorily during system unbalances, reactor energizations, external faults, off-nominal frequency, and switch onto fault scenarios, etc.
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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 | 0.000 |
| 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".