Locating Inter-Turn Faults in Transformer Windings Using Isometric Feature Mapping of Frequency Response Traces
Bibliographic record
Abstract
Power transformers usually confront various mechanical and electromagnetic stresses during an operation that may lead to defects in their windings. The short circuit in the windings is one of those severe defects. Early detection of short-circuits is necessary as extra heating in the shorted location can lead to progressive damage in windings insulation. Frequency response analysis (FRA) is a well-known method to diagnose short-circuits in transformers. Despite the accuracy of FRA, the interpretation of the obtained frequency response traces (FRTs) is still an intricate task. Due to the unknown impact of faults on FRTs, extracting efficient features from such traces is necessary for the interpretation of transformer's frequency response. In this article, an isometric feature mapping (Isomap) is used as a nonlinear dimensionality reduction technique to locate interturn faults in transformer windings due to its capability of capturing the nonlinear phenomena in FRT of power transformers. It is revealed that, after constructing the isometric mapping for a transformer, there is no need for any expertise to detect fault location even in nondirect (high impedance) short-circuits. In other words, it can be the first step for the automated interpretation of FRA of power transformers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".