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Prediction of Icing on Wind Turbines Based on SCADA Data via Temporal Convolutional Network

Author

Listed:
  • Yujie Zhang

    (Center for Wind Energy, Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA)

  • Nasser Kehtarnavaz

    (Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA)

  • Mario Rotea

    (Center for Wind Energy, Department of Mechanical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA)

  • Teja Dasari

    (Xcel Energy, Minneapolis, MN 55401, USA)

Abstract

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6 % for future times of up to 48 h.

Suggested Citation

  • Yujie Zhang & Nasser Kehtarnavaz & Mario Rotea & Teja Dasari, 2024. "Prediction of Icing on Wind Turbines Based on SCADA Data via Temporal Convolutional Network," Energies, MDPI, vol. 17(9), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2175-:d:1387672
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    References listed on IDEAS

    as
    1. Bai, Xinjian & Tao, Tao & Gao, Linyue & Tao, Cheng & Liu, Yongqian, 2023. "Wind turbine blade icing diagnosis using RFECV-TSVM pseudo-sample processing," Renewable Energy, Elsevier, vol. 211(C), pages 412-419.
    2. Cheng Tao & Tao Tao & Xinjian Bai & Yongqian Liu, 2023. "Wind Turbine Blade Icing Prediction Using Focal Loss Function and CNN-Attention-GRU Algorithm," Energies, MDPI, vol. 16(15), pages 1-15, July.
    3. Swenson, Lauren & Gao, Linyue & Hong, Jiarong & Shen, Lian, 2022. "An efficacious model for predicting icing-induced energy loss for wind turbines," Applied Energy, Elsevier, vol. 305(C).
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