Machine Learning Based Online Monitoring of Step-Up Transformer Assets in Electrical Generating Stations
Bibliographic record
Abstract
Electric utilities undertake a significant cost to maintain the health of their assets every year. In recent years, in BC Hydro, one of the strategies in maintaining generating stations has been to shift burden from preventative maintenance to predictive maintenance when safely and reliably applicable. This approach could help utilities to reduce the cost of preventative maintenance programs and further rely on predictive measures to assess the health risk of assets and only execute physical interventions when the assessed risk compels. In this paper, the authors share BC Hydro's experience in using machine learning for predictive online monitoring of step-up transformers at electrical generating stations. In addition to high level statistics of the machine learning based predictive online monitoring program, the paper presents a detailed example where predictive monitoring prevented financial damage to a transformer and mitigated fast reduction of remaining life of the asset possibly saving the company millions of dollars.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".