Partial Discharge Localization Using Electromagnetic Time Reversal: A Performance Analysis
Bibliographic record
Abstract
In this study, first, a comparison on the application of electromagnetic time reversal (EMTR) and time difference of arrival (TDoA) in partial discharge localization in power transformers is presented. A two-dimensional finite-difference time-domain simulation is used to calculate the signal recorded by the sensors. Results show that, in a transformer tank excluding its windings, both methods yield similar results in terms of location accuracy, although the EMTR method only needs one sensor to localize the partial discharge (PD) source while the TDoA method needs at least three sensors in the 2D localization problem. However, the presence of transformer windings leads to a degradation of the performance of the TDoA method if the line of sight from the source to the sensor is blocked by any of the winding blocks. On the other hand, the presence of the transformer windings has an effect on the localization of PD sources that occur between two adjacent phase windings when the distance between the outer winding distances is shorter than the minimum wavelength,$\lambda _{\mathrm {min}}$. The degradation is directly caused by the diffraction limit. It is shown that, if the distance between two adjacent phase windings is greater than$\lambda _{\mathrm {min}}$, the EMTR process can locate PD sources occurring between two adjacent phase windings with acceptable accuracy. A case of occurrence of PDs in close proximity (less than$\lambda _{\mathrm {min}}$/2) to a single metallic object is analyzed both numerically and experimentally. The analysis reveals that although a degradation in the accuracy of the localization is observed compared to the case of longer distances between the PD source and the metallic object, a reasonable localization error of 10 mm (corresponding to$\lambda _{\mathrm {min}}$/10) is obtained.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 0.001 |
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".