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Abnormal Detection of Wind Turbine Based on SCADA Data Mining

Author

Listed:
  • Liang Tao
  • Qian Siqi
  • Yingjuan Zhang
  • Huan Shi

Abstract

In order to reduce the curse of dimensionality of massive data from SCADA (Supervisory Control and Data Acquisition) system and remove data redundancy, the grey correlation algorithm is used to extract the eigenvectors of monitoring data. The eigenvectors are used as input vectors and the monitoring variables related to the unit state as output vectors. The genetic algorithm and cross validation method are used to optimize the parameters of the support vector regression (SVR) model. A high precision prediction is carried out, and a reasonable threshold is set up to alarm the fault. The condition monitoring of the wind turbine is realized. The effectiveness of the method is verified by using the actual fault data of a wind farm.

Suggested Citation

  • Liang Tao & Qian Siqi & Yingjuan Zhang & Huan Shi, 2019. "Abnormal Detection of Wind Turbine Based on SCADA Data Mining," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, August.
    • Handle: RePEc:hin:jnlmpe:5976843
    DOI: 10.1155/2019/5976843
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    Cited by:

    1. Qian, XiaoYi & Sun, TianHe & Zhang, YuXian & Wang, BaoShi & Awad Gendeel, Mohammed Altayeb, 2023. "Wind turbine fault detection based on spatial-temporal feature and neighbor operation state," Renewable Energy, Elsevier, vol. 219(P1).
    2. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Xiangqing Yin & Yi Liu & Li Yang & Wenchao Gao, 2022. "Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting," Energies, MDPI, vol. 15(17), pages 1-22, August.

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