Winding turn‐to‐turn short‐circuit diagnosis using FRA method: sensitivity of measurement configuration
Why this work is in the frame
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Bibliographic record
Abstract
Frequency response analysis (FRA) is increasingly being accepted as an effective technique to diagnose transformer faults. Transformer electric parameters are affected by such faults in a complex manner, and there is yet no standard approach for interpretation of FRA results. Most studies have focused on diagnosing winding and core deformations, but subtle defects in the winding insulation and turn‐to‐turn short circuits can develop into a more serious fault, and their early diagnosis is equally important. Furthermore, there are several test configurations which have different sensitivities to different faults. This study reports a study where the winding input impedance is measured to diagnose turn‐to‐turn short circuits using different measurement configurations. A comparison is made between the sensitivities of each measurement configuration to faults of increasing severity. It is found that this fault is detected in the low‐ and mid‐frequency regions as significant reduction in impedance and a shift in resonance peaks towards high frequencies. The results, analysed using different statistical parameters, indicate differences in sensitivities with different levels of short circuits. Marginal variations were found between the sensitivities of statistical parameters in different frequency regions. The study provides useful contribution into interpretation of FRA signatures for turn‐to‐turn short‐circuit diagnosis of transformers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it