AI-driven single-end partial discharge localization in power cables based on time domain reflectometry and transfer function analyses
Bibliographic record
Abstract
Accurate localization of partial discharge (PD) in power cables is critical for minimizing downtime and associated costs. Therefore, this paper presents a single-end localization method that simplifies implementation by avoiding the complexities of double-sided or distributed schemes. A fundamental challenge for online monitoring systems based on a single-end measurement scheme is the accurate and autonomous identification of incident pulses and their corresponding reflections, particularly in environments where impulse noise and PD-like interference are present and may resemble actual PD pulses, making it difficult to distinguish true events from interfering pulses. In this regard, this paper proposes a method based on the traveling wave characteristics and transfer function (TF) analysis to pinpoint the PD source accurately, even in challenging conditions such as multi-path propagation, impulse noise, and simultaneous PD events. To achieve this, a cable-specific attenuation characteristic is developed and incorporated within a two-step signal segmentation algorithm, and then the U-Net model is employed to estimate PD pulses’ arrival time precisely. Additionally, the proposed method provides a statistical analysis of its maximum localization capability based on the noise level and cable length. The performance of the method is assessed under both homogeneous and inhomogeneous cable configurations. The results demonstrate a localization error of less than 1% for a 1.5 km cable.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".