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Summary
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This study introduces a novel data processing workflow for wind farms, enhancing data quality through advanced anomaly detection and multi-segment interpolation. The method significantly improves predictive alert accuracy and model stability.

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Area of Science:

  • Renewable Energy Engineering
  • Data Science
  • Signal Processing

Background:

  • Wind turbine data is susceptible to noise and interference in remote environments.
  • Data corruption impacts critical tasks like predictive maintenance and diagnostics.
  • Effective data preprocessing is crucial for reliable wind farm operations.

Purpose of the Study:

  • To develop a comprehensive data processing workflow for wind farm data.
  • To enhance the accuracy of anomaly detection and data interpolation.
  • To improve the performance of downstream predictive models.

Main Methods:

  • Proposed a fuzzy voting-based outlier detection method using multiple anomaly detectors.
  • Introduced a multi-segment data interpolation technique with dynamic thresholding.
  • Employed forward-backward LOESS for middle gaps and thermal card filling for large gaps.

Main Results:

  • The multi-segment interpolation method reduced Mean Absolute Error (MAE), Mean Squared Relative Error (MSRE), and Root Squared Error (RSE) by 24%, 7.1%, and 8.2% respectively, compared to LSTM.
  • The complete workflow improved DLinear model performance, increasing F1 score by 3.8-19.1% and Accuracy by 2.3-13.3% on test data.

Conclusions:

  • The proposed data processing workflow significantly enhances wind field data quality.
  • The method improves the precision, stability, and convergence speed of early warning models.
  • This approach offers an effective solution for managing noisy and incomplete wind turbine data.