Improved power curve monitoring of wind turbines
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
Wind turbine power output monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure. This study examines common Supervisory Control And Data Acquisition data over a period of 20 months. It is common to have more than 150 signals acquired by Supervisory Control And Data Acquisition systems, and applying all is neither practical nor useful. Thus, to address the issue, correlation coefficients analysis has been applied in this work to reveal the most influential parameters on wind turbine active power. Then, radial basis function and multilayer perception artificial neural networks are set up, and their performance is compared in two static and dynamic states. The proposed combination of the feature selection method and the dynamic multilayer perception neural network structure has performed well with favorable prediction error levels compared to other methods. Thus, the combination may be a valuable tool for turbine power curve monitoring.
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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 | 0.000 | 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 it