CapsPDNet: Optimized Capsule Network for Predicting Insulator Discharges Using UHF Signals
Bibliographic record
Abstract
Outdoor insulation systems often suffer from partial discharge (PD) faults that compromise the reliability of electrical grids. This paper proposes an integrated approach combining a non-invasive downsized discone antenna to capture ultra-high-frequency signals and an optimized capsule network (CapsPDNet) for robust PD fault prediction. The CapsPDNet is chosen as it overcomes the information loss associated with pooling operations and leverages vector representations, providing more nuanced predictions than scalar values, especially when the predictions are made across different antenna locations. Three typical PD fault types are targeted, surface discharges on ceramic and polymeric materials, internal discharges, and corona discharges, and then multiple signal processing techniques are evaluated to determine the most effective feature extraction and reconstruction technique. Experimental results show that the proposed model achieves up to a 18.89% improvement in PD fault prediction accuracy over benchmark approaches, demonstrating high generalizability and scalability when considering different antenna locations. The patented antenna used in this study offers a compact size through a novel size reduction technique, covers a wide bandwidth, and exhibits high sensitivity to PD activities. Additionally, discrete wavelet decomposition (DWT) is chosen as the feature extractor as it provides high-importance features with the lowest computational time, offering a more reliable and scalable solution for early PD fault detection and real-time condition monitoring of insulation systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".