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A Parameter Selection Method for Wind Turbine Health Management through SCADA Data

Author

Listed:
  • Mian Du

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
    China Electric Power Research Institute, Beijing 100192, China)

  • Jun Yi

    (China Electric Power Research Institute, Beijing 100192, China)

  • Peyman Mazidi

    (Department of Electric Power and Energy Systems (EPE), KTH Royal Institute of Technology, Stockholm 10044, Sweden)

  • Lin Cheng

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Jianbo Guo

    (China Electric Power Research Institute, Beijing 100192, China)

Abstract

Wind turbine anomaly or failure detection using machine learning techniques through supervisory control and data acquisition (SCADA) system is drawing wide attention from academic and industry While parameter selection is important for modelling a wind turbine’s condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. First, the copula is proven to be capable of simplifying the estimation of mutual information. Then an empirical copulabased mutual information estimation method (ECMI) is introduced for application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

Suggested Citation

  • Mian Du & Jun Yi & Peyman Mazidi & Lin Cheng & Jianbo Guo, 2017. "A Parameter Selection Method for Wind Turbine Health Management through SCADA Data," Energies, MDPI, vol. 10(2), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:253-:d:90908
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    References listed on IDEAS

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    1. Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
    2. Castellani, Francesco & Astolfi, Davide & Sdringola, Paolo & Proietti, Stefania & Terzi, Ludovico, 2017. "Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment," Applied Energy, Elsevier, vol. 185(P2), pages 1076-1086.
    3. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
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    Cited by:

    1. Kai Ma & Shubing Hu & Jie Yang & Chunxia Dou & Josep M. Guerrero, 2017. "Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach," Energies, MDPI, vol. 10(5), pages 1-16, May.

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