A Convolutional Neural Network-Based Model for Multi-Source and Single-Source Partial Discharge Pattern Classification Using Only Single-Source Training Set
Bibliographic record
Abstract
Classification of the sources of partial discharges has been a standard procedure to assess the status of insulation in high voltage systems. One of the challenges while classifying these sources is the decision on the distinct properties of each one, often requiring the skills of trained human experts. Machine learning offers a solution to this problem by allowing to train models based on extracted features. The performance of such algorithms heavily depends on the choice of features. This can be overcome by using deep learning where feature extraction is done automatically by the algorithm, and the input to such an algorithm is the raw input data. In this work, an enhanced convolutional neural network is proposed that is capable of classifying single sources as well as multiple sources of partial discharges without introducing multiple sources in the training phase. The training is done by using only single-source phase-resolved partial discharge (PRPD) patterns, while testing is performed on both single and multi-source PRPD patterns. The proposed model is compared with single-branch CNN architecture. The average percentage improvements of the proposed architecture for single-source PDs and multi-source PDs are 99.6% and 96.7% respectively, compared to 96.2% and 77.3% for that of the traditional single-branch CNN architecture.
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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 | 0.000 |
| 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".