Building a hydro-generator rotor temperature virtual sensor using machine-learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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