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Correlation Investigation of Wind Turbine Multiple Operating Parameters Based on SCADA Data

Author

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  • Huifan Zeng

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

  • Juchuan Dai

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

  • Chengming Zuo

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

  • Huanguo Chen

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

  • Mimi Li

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

  • Fan Zhang

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

Abstract

The primary wind turbines’ in-service performance evaluation method is mining and analyzing the SCADA data. However, there are complex mathematical and physical relationships between multiple operating parameters, and so far, there is a lack of systematic understanding. To solve this issue, the distribution of wind turbines’ operating parameters was first analyzed according to the characteristics of the energy flow of wind turbines. Then, the correlation calculation was performed using the Spearman correlation coefficient method based on the minute-level data and second-level data. According to the numerical characteristics of the nacelle vibration acceleration, the data preprocessing technology sliding window maximum (SWM) was proposed during the calculation. In addition, taking temperature correlation as an example, two-dimensional scatter (including single-valued scatter) and three-dimensional scatter features were combined with numerical analysis and physical mechanism analysis to understand the correlation characteristics better. On this basis, a quantitative description model of the temperature characteristics of the gearbox oil pool was constructed. Through this research work, the complex mathematical and physical relationships among the multi-parameters of the wind turbines were comprehensively obtained, which provides data and theoretical support for the design, operation, and maintenance.

Suggested Citation

  • Huifan Zeng & Juchuan Dai & Chengming Zuo & Huanguo Chen & Mimi Li & Fan Zhang, 2022. "Correlation Investigation of Wind Turbine Multiple Operating Parameters Based on SCADA Data," Energies, MDPI, vol. 15(14), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5280-:d:867984
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    References listed on IDEAS

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    Cited by:

    1. Adaiton Oliveira-Filho & Ryad Zemouri & Philippe Cambron & Antoine Tahan, 2023. "Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model," Energies, MDPI, vol. 16(12), pages 1-21, June.
    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.
    3. Dai, Juchuan & Li, Mimi & Zhang, Fan & Zeng, Huifan, 2024. "Field load testing of wind turbines based on the relational model of strain vs load," Renewable Energy, Elsevier, vol. 221(C).

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