Current state of transformer FRA interpretation
Bibliographic record
Abstract
Measurement of the frequency response, from a few Hz to a few MHz, is now commonly used in the transformer industry for the condition assessment of transformer windings and has demonstrated its sensitivity for detecting various mechanical and electrical failure modes. The present generally applied practice for interpretation is visual comparison of frequency responses, either with a previous measurement on the same or an identical unit, or between the phases of a three-phase transformer. Examples of curve comparison for typical mechanical and electrical failure modes have previously been published in CIGRE and IEEE guides. Over the last 15 years, numerous technical papers have been published regarding the interpretation of the results in an aim to make it more objective and quantitative. In 2016, CIGRE initiated a new working group titled “Objective interpretation methodology for the condition assessment of transformer windings using Frequency Response Analysis (FRA)”. This paper, written on behalf of the new working group, reviews the basics of FRA interpretation and summarizes the state-of-the-art regarding the potential methods that can be applied to achieve a more objective and quantitative interpretation of the results.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".