Development of a Methodology to Automatically Identify Active PD Sources from Phase Resolved Partial Discharge Patterns
Bibliographic record
Abstract
Hydro-Québec is working on the development of a prognostic approach for hydrogenerators based on the modelling of failure mechanisms in the form of graph. The failure mechanism graph consists of several cause-and-effect chains formed by the combination of sequential physical degradation states that ultimately lead to a failure. The first step of the approach is to automatically identify the active failure mechanisms based on the analysis of observable symptoms extracted from diagnostic tools. For the stator winding of hydrogenerators, more than 100 failure mechanisms have been consigned and most of them involve the presence of partial discharge (PD) activity. PD measurements as a diagnostic tool for hydrogenerators have been performed over the last 30 years at Hydro-Québec. During these years, a large PD database has been built up, including the results from two measurement techniques: 2D Partial Discharge Analyzer (PDA) as well as Phase Resolved Partial Discharge (PRPD) patterns. These two measurement techniques provide complementary results that will be combined in the prognostic approach. In this context, the objective of this paper is to present the development of a two-phase methodology aiming to automatically identify the active PD sources from PRPD patterns using machine learning techniques. The first phase of the proposed methodology is the preprocessing where filters have been applied to adjust the phase shift of each PRPD pattern. Then, two U-Net learning models combined with a Convolutional Neural Network (CNN) learning model are used to identify the presence of gap PD activity and to extract the main active PD sources. The next and final phase of the methodology is the PD source recognition which is based on the aggregation of results from six CNN learning models. These CNN models were trained on different features extracted from the preprocessed PRPD patterns resulting from the first phase of the methodology. Results showed that using the U-net learning model is very effective in separating PD sources and that the precision of the PD source recognition is significantly improved by combining the six CNN models instead of using each individual CNN learning model.
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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 | 0.002 | 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) | 0.002 | 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 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".