Current state of transformer FRA interpretation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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