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Research on Anomaly Detection of Wind Farm SCADA Wind Speed Data

Author

Listed:
  • Wu Wen

    (School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China)

  • Yubao Liu

    (Precision Regional Earth Modeling and Information Center, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Rongfu Sun

    (Electric Power Dispatch Center, Jibei Electric Power Company, Beijing 100083, China)

  • Yuewei Liu

    (Research Applications Laboratory of National Center for Atmospheric Research, Boulder, CO 80305, USA)

Abstract

Supervisory control and data acquisition (SCADA) systems are critical for wind power grid integration and wind farm operation and maintenance. However, wind turbines are affected by regulation, severe weather factors, and mechanical failures, resulting in abnormal SCADA data that seriously affect the usage of SCADA systems. Thus, strict and effective data quality control of the SCADA data are crucial. The traditional anomaly detection methods based on either “power curve” or statistical evaluation cannot comprehensively detect abnormal data. In this study, a multi-approach based abnormal data detection method for SCADA wind speed data quality control is developed. It is mainly composed of the EEMD (Ensemble Empirical Mode Decomposition)-BiLSTM network model, wind speed correlation between adjacent wind turbines, and the deviation detection model based on dynamic power curve fitting. The proposed abnormal data detection method is tested on SCADA data from a real wind farm, and statistical analysis of the results verifies that this method can effectively detect abnormal SCADA wind data. The proposed method can be readily applied for real-time operation to support an effective use of SCADA data for wind turbine control and wind power prediction.

Suggested Citation

  • Wu Wen & Yubao Liu & Rongfu Sun & Yuewei Liu, 2022. "Research on Anomaly Detection of Wind Farm SCADA Wind Speed Data," Energies, MDPI, vol. 15(16), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5869-:d:887092
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    References listed on IDEAS

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    1. Yingying Zhao & Dongsheng Li & Ao Dong & Dahai Kang & Qin Lv & Li Shang, 2017. "Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data," Energies, MDPI, vol. 10(8), pages 1-17, August.
    2. Thapar, Vinay & Agnihotri, Gayatri & Sethi, Vinod Krishna, 2011. "Critical analysis of methods for mathematical modelling of wind turbines," Renewable Energy, Elsevier, vol. 36(11), pages 3166-3177.
    3. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
    4. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Modeling wind-turbine power curve: A data partitioning and mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 1-8.
    5. Kong, Ziqian & Tang, Baoping & Deng, Lei & Liu, Wenyi & Han, Yan, 2020. "Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units," Renewable Energy, Elsevier, vol. 146(C), pages 760-768.
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    Cited by:

    1. Jayroop Ramesh & Sakib Shahriar & A. R. Al-Ali & Ahmed Osman & Mostafa F. Shaaban, 2022. "Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System," Energies, MDPI, vol. 15(21), pages 1-19, October.

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