Wind Farm Generators Thermal Condition Monitoring Based on Long Short-Term Memory
Bibliographic record
Abstract
In recent decades, wind farms were the focal point of investing due to their considerable advantages over conventional fossil fuel-based power plants. Since a lot of the wind farms are installed more than 10 years ago, it is essential to develop a practical and cost-effective condition monitoring strategy that can address most of the common failures in wind farms using the data provided by existing SCADA systems. The need for a modern condition monitoring strategy coincides with the emergence of Deep Learning (DL) in the recent decade. In this paper, a novel condition monitoring is proposed to monitor the temperature of generator windings in a wind farm using Long Short-Term Memory (LSTM) model which is a Recurrent Neural Network (RNN) method. This DL model is a practical and unique choice for prediction due to its ability for considering long-term dependencies among input sequential features, which in this case are the provided time-series datasets by SCADA system. The novel proposed model in this paper is evaluated in healthy and abnormal operation modes of a wind farm in Quebec Province, Canada as case scenarios. Moreover, the error signal is calculated between real and predicted signals to evaluate the proper performance of the model. Finally, this model is compared with Multiple Linear Regression model to show the effectiveness and high precision of the proposed model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".