Building a hydro-generator rotor temperature virtual sensor using machine-learning
Bibliographic record
Abstract
This paper describes the development and application of a virtual sensor for estimating rotor temperatures in hydroelectric generators using machine learning techniques. Rotor temperature is a critical factor in hydrogenerator performance and lifespan, with poor assessments of real temperature limits leading to production losses or accelerated degradation. The proposed virtual sensor leverages operational signals from the continuous monitoring system (CMS) and is trained on data from instrumented units, offering an alternative to costly and intrusive direct measurements. Three machine learning models were tested: a multi-layer perceptron (MLP), a recurrent neural network-gated recurrent unit (RNN-GRU) and a long short-term memory (LSTM) model. Two strategies were used for validation: continuous monitoring of the same unit and transfer learning between units of similar design. The LSTM model achieved prediction errors within ±1°C during continuous monitoring and ±2°C during transfer learning. The model’s ability to generalize across varying cooling temperatures and operating conditions was also validated. The virtual sensor provides accurate rotor temperature estimates, reducing reliance on physical instrumentation and enabling continuous monitoring of non-instrumented units.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".