Evaluation of Electrical Tree Length Information Based on Partial Discharge Signal
Bibliographic record
Abstract
Electrical treeing is a common phenomenon associated with insulation degradation. When these trees extend to the ground side, cable breakdown occurs. To ensure safe transmission in power systems, it is crucial to quantify the growth length of electrical trees using specific indicators. However, existing PRPD spectra exhibit limited capability in characterizing the length of electrical branches, as phase parameters become indistinguishable under varying branch lengths. Currently, the most widely used methods for characterizing partial discharge phenomena are the T-F map and PRPD diagrams. According to experimental results, the$T-F$map did not show significant differences as the electrical tree grew. In the experiment, the PRPD diagrams only exhibited amplitude variations, which were heavily influenced by voltage levels. Therefore, these methods were deemed unsuitable for accurately characterizing electrical tree growth. Given that the growth of electrical trees exhibits chaotic characteristics, this study integrates both nonlinear and linear quantities in partial discharge analysis to identify differences in partial discharge behavior at various growth stages. In this study, after normalizing the original partial discharge signal, amplitude information and sign information were extracted. The amplitude information was treated as a nonlinear quantity, while the sign information was considered a linear quantity. Both were computed using the DFA algorithm and subsequently plotted on a graph, with the amplitude information represented on the$\boldsymbol{x}$-axis and the sign information on the$y$-axis. The results indicate that as the electrical tree progresses, the data tend to migrate towards the upper-left quadrant. The experimental results demonstrate that this method can, to some extent, determine the growth of electrical trees by measuring partial discharge signals during their growth process.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".