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SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System

Author

Listed:
  • Huanguo Chen

    (Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Chao Xie

    (Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Juchuan Dai

    (School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Enjie Cen

    (Hangzhou Dingchuan Information Technology Co., Ltd., Hangzhou 310016, China)

  • Jianmin Li

    (Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

Due to the complex and variable conditions under which wind turbines operate, existing working condition classification methods are inadequate for condition assessment of the main transmission system. Because working conditions are too few after classification, it cannot effectively describe the complex and variable working conditions of wind turbine. This can lead to high false-alarm rates in the condition monitoring, which affect normal operations. This paper proposes a working condition classification method for the main transmission system of wind turbines based on supervisory control and data acquisition (SCADA) data. Firstly, correlation analysis of SCADA data acquired by wind farm is used to select the parameters relevant to the main transmission system. Secondly, according to the wind turbine control principle, the working conditions are initially divided into four phases: shutdown, start-up, maximum wind energy tracking, and constant speed. The k -means clustering algorithm is used to subdivide the maximum wind energy-tracking phase and constant speed phase, which account for a larger proportion of the working conditions, to achieve better classification. Finally, a case study is used to demonstrate the calculation of alarm thresholds and alarm rates for each working condition. The results are compared with the direct use of k -means clustering for working condition classification. It is concluded that the proposed method can significantly reduce the false-alarm rate of the vibration detection process.

Suggested Citation

  • Huanguo Chen & Chao Xie & Juchuan Dai & Enjie Cen & Jianmin Li, 2021. "SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System," Energies, MDPI, vol. 14(21), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7043-:d:666418
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    References listed on IDEAS

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    1. Zhu, Xu & Zhang, Shuai & Jin, Xinqiao & Du, Zhimin, 2020. "Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency," Energy, Elsevier, vol. 213(C).
    2. Dai, Juchuan & Tan, Yayi & Shen, Xiangbin, 2019. "Investigation of energy output in mountain wind farm using multiple-units SCADA data," Applied Energy, Elsevier, vol. 239(C), pages 225-238.
    3. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
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    Cited by:

    1. Davide Astolfi & Francesco Castellani, 2022. "Editorial on the Special Issue “Wind Turbine Monitoring through Operation Data Analysis”," Energies, MDPI, vol. 15(10), pages 1-4, May.
    2. Chengming Zuo & Juchuan Dai & Guo Li & Mimi Li & Fan Zhang, 2023. "Investigation of Data Pre-Processing Algorithms for Power Curve Modeling of Wind Turbines Based on ECC," Energies, MDPI, vol. 16(6), pages 1-24, March.

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