Separation and Classification of Concurrent Partial Discharge Signals Using Statistical-Based Feature Analysis
Bibliographic record
Abstract
In this paper, an algorithm for feature extraction and classification of high-pressure gas insulation system defects based on statistical analysis of time-domain parameters of partial discharge (PD) signals is presented. The algorithm focuses on the measurement and interpretation of PD signals in the time domain in completion of our previous paper on multiple-source phase resolved patterns. In this procedure, the PD measurements are conducted in different artificial defects that commonly happen in gas-insulated substations (GIS) such as corona, moving particles, floating electrodes, and metallic protrusions. To overcome the noise problem, wavelet transform (WT) technique is applied on the recorded signals. The PD pulse waveform parameters, namely rise time, fall time, slew rate, and pulse width are calculated, investigated and used as the discriminative features to represent each type of PD signals. The separation of PD sources is implemented based on Weibull distribution and K-means unsupervised clustering technique. The higher level statistical based features of each cluster are calculated, studied and used as the inputs of a kernel support vector machine (KSVM) classifier in order to classify multiple PD sources in a robust way. The results of this work demonstrate that the presented probabilistic diagnostic algorithm to extract features from time-domain PD pulse waveforms and their corresponding probabilistic distribution can be employed to cluster and classify PD signals based on their source of origin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".