Fast identification of partial discharge sources using blind source separation and kurtosis
Bibliographic record
Abstract
A technique for the fast identification of partial discharge (PD) sources is proposed for the detection of mechanical failure or damage to insulation materials by using wireless remote control and monitoring systems in substations. An estimation of the number of PD sources can help to evaluate the insulation performance and lifetime of power equipment. Multiple PD sources can be generated during the operating voltage where their electromagnetic radiations are highly impulsive, non‐Gaussian noise and the resulting probability distribution function is heavy‐tailed. Multiple PD sources can be estimated by their electromagnetic radiations via blind source separation (BSS) and measuring the excess kurtosis using low‐cost wireless intelligent electronic devices. The efficiency and performance of the proposed method is demonstrated by simulating PD sources based on the spatial Poisson point process where the number of sources is a random variable not known by the receiver. Assuming non‐white and decorrelated or non‐Gaussian and independent sources, results show that the number of significant PD sources can be estimated with low error rate. Underdetermined problems in BSS can affect performances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".