Hydropower unit digital twin calibration using monitoring data and blackbox optimization
Bibliographic record
Abstract
Abstract One-dimensional models can enable the assessment of the dynamic behavior of hydropower units during transient operation with minimal computational resources. Since these models do not consider the full three-dimensional flow in hydraulic piping systems, their predictions rely on static performance characteristics obtained typically through reduced-scale measurements. These measurements usually cover only a small portion of the complete operating range of the machine, making it challenging to simulate transient events such as start-up, shutdown, or load rejections with high fidelity. To address this issue, we propose a novel approach for calibrating one-dimensional physics-based models of hydropower units by combining monitoring data with blackbox optimization. Specifically, the performance characteristic curves feeding the one-dimensional model of the power plant are represented by a polynomial whose parameters are optimized by minimizing the difference between simulation results and experimental data. We demonstrate that the proposed method is suitable for a 50 MW Kaplan turbine considering various startup scenarios, including different guide vane and blade opening sequences. The optimization was conducted using different combinations of training and test datasets to assess the validity of the calibrated models. Good agreement between simulation and experimental data was obtained using only a few startup sequences in the training dataset which demonstrate the robustness of the proposed methodology. This may pave the way for the calibration of hydropower unit digital twins for unit monitoring and anomaly detection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".