Improvement of a Hydrogenerator Prognostic Model by using Partial Discharge Measurement Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Availability and performance of hydrogenerators are key features that have driven electrical utilities to implement monitoring and diagnostic methods in order to evolve to condition based maintenance (CBM). Ten years ago, Hydro-Quebec has implemented a home-built web-basedapplication, called MIDA, to cover most of its power plants. MIDA centralizes diagnostic data from several tools, aggregates all diagnostic results and calculates a health index for each hydrogenerators. Data from MIDA used in conjunction with PHM techniques can feed a prognostic model that will provide useful equipment information and lead to the implementation of predictive maintenance. The prognostic framework used for hydrogenerators is based on a failure mechanism and symptom analysis (FMSA) approach. For the stator, a major component of hydrogenerators, more than 100 failure mechanisms have been consigned in the form of causal trees or graphs. A large number of these failure mechanisms involve the presence of partial discharges (PD) before failure occurs. At Hydro-Quebec, PD measurements on hydrogenerators have been carried out over the past 30 years and a significant PD database is integrated in MIDA. The analysis of this huge amount of data is of paramount importance to understand the behavior and evolution of the discharge activity in order to build a robust prognostic approach using physics based as well as data driven models. To that end, this paper presents case studies that shed some light on key features related to the evolution of PD activity in hydrogenerators. The paper discusses how to use this data in the prognostic model to assess warning signs before failure occurs.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it