An Intelligent Framework for Optimal Feature Selection of PD Signals Produced by Electrical Defects in MV Switchgear
Bibliographic record
Abstract
This study presents a novel feature-wise non-linear least absolute shrinkage and selection operator (Lasso) based technique for optimal feature selection of high-dimensional features in Partial discharges (PD) data. Laboratory conditions are employed to develop four insulation defects in medium voltage (MV) switchgear, extracting raw PD data. An experimental platform comprising a hybrid PD detection technique is employed to capture 4000 conventional and 4000 unconventional PD signals. Also, 4000 cumulative energy (CE) signals are generated from unconventional PD signals. From this dataset comprising 12000 signals, 35 typical PD features, 490 two-dimensional features, and 777 three-dimensional features are achieved. Finally, a novel feature-wise non-linear Lasso-based feature selection technique is implemented for optimal feature selection of PD data. The effectiveness of the proposed method is evaluated through different classifiers. The proposed technique demonstrates exceptional performance in feature selection for PD data in MV switchgear with the potential for application to other power system components.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".