Towards automated statistical partial discharge source classification using pattern recognition techniques
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Bibliographic record
Abstract
This study presents a comprehensive review of the automated classification in partial discharge (PD) source identification and probabilistic interpretation of the classification results based on the relationship between the variation of the phase‐resolved PD (PRPD) patterns and the source of the PD. The proposed automated classification system consists of modern, high‐performance statistical feature extraction methods and classifier algorithms. Their application in online monitoring and recognition of the PD patterns is investigated based on their low‐processing time and high‐performance evaluation. The application of modern statistical algorithms and pre‐processing methods configured in this automated classification system improves the pattern recognition accuracy of the different PD sources that are suitable to be employed in different high‐voltage (HV) insulation media. To evaluate the performance of the different combinations of the feature extraction/classier pairs, laboratory setups are designed and built that simulate various types of PDs. The test cells include three sources of PD in , two sources of PD in transformer oil, and corona in the air. Data samples for different classes of PD sources are captured under two levels of voltage and two different levels of noise. The results of this study evaluate the suitability of the proposed classification systems for probabilistic source identification in various insulation media. Furthermore, of importance to the problem of the PD source identification is to assign a ‘degree of membership’ to each PRPD pattern, besides assigning a class label to it. Some of the classifier algorithms studied in this study, such as fuzzy classifiers, are not only able to show high classification accuracy rate, but they also calculate the ‘degree of membership’ of a sample to a class of data. This enables probabilistic interpretation of a new PRPD pattern that is being classified. The determination of the degree of membership for future PRPD samples allows safer decision making based on the risk associated with the different sources of PD in HV apparatus.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it