Novel Antenna for Partial Discharge Detection and Classification: A Convolutional Neural Network-Based Deep Learning Approach
Bibliographic record
Abstract
Inspection of high voltage (HV) devices using ultra-high frequency (UHF) sensors has been predominantly employed for partial discharge (PD) detection and classification. This work reports implementing and testing a coplanar waveguide (CPW)-fed annular monopole antenna for PD detection. The 3D Maxwell solver of COMSOL multi-physics is used in this paper to optimize the antenna parameters and improve its performance. The original size of the antenna is reduced by about 47% utilizing structural symmetry and current resonances. The proposed antenna exhibits a wide bandwidth over frequencies ranging between 0.5 GHz - 3 GHz (except at 0.6 GHz, 1.2 GHz, and 2.75 GHz) due to the applied size reduction, using a maximum reflection coefficient of -10 dB (based on measurements). Nonetheless, the antenna performance is still effective over the full UHF range (considering that -6 dB is sufficient to detect PD activities). The effectiveness of the proposed antenna in PD detection is verified by testing the antenna’s performance against three common types of PD defects, namely, sharp point-to-ground discharge, surface discharge, and internal discharge. Furthermore, deep learning is implemented to classify the three defects with a total classification accuracy of 96%.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".