Machine-Learning Supported Localization of Partial Discharges in Real-Size Transformer Winding Using Noninvasive Capacitively- Coupled Pulse Injection
Bibliographic record
Abstract
Partial discharges (PDs) in transformer windings can lead to failures, causing power grid instability and outages. Therefore, early detection and accurate localization of PD incidents are crucial. This article introduces an electrical approach for PD localization in transformer windings. The approach involves the noninvasive injection of pulses (resembling PD) into the winding and recording the propagated signals at the winding terminals (i.e., line bushing and neutral ends). Noninvasive PD injection is performed using a capacitive coupling due to the absence of electrical access to transformer winding conductors. Next, features are extracted from the recorded signals to correlate them with the location of the injection point. This study investigates the effect of the PD pulse injection point on its response waveform recorded at the terminals to identify features corresponding to the PD source using a single measurement. Certain features of captured signals are highly correlated with PD location. The proposed approach establishes an accurate disk-to-disk localization model, particularly for large transformers. It employs a supervised classifier for PD localization. The performance of the proposed approach is verified using experimental measurements.
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".