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Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Author

Listed:
  • Davide Astolfi

    (Department of Engineering, University of Perugia, 06125 Perugia, Italy)

  • Francesco Castellani

    (Department of Engineering, University of Perugia, 06125 Perugia, Italy)

  • Andrea Lombardi

    (ENGIE Italia, Via Chiese, 20126 Milano, Italy)

  • Ludovico Terzi

    (ENGIE Italia, Via Chiese, 20126 Milano, Italy)

Abstract

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

Suggested Citation

  • Davide Astolfi & Francesco Castellani & Andrea Lombardi & Ludovico Terzi, 2021. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring," Energies, MDPI, vol. 14(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1105-:d:502178
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    References listed on IDEAS

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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. 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.
    3. Davide Astolfi & Ravi Pandit & Andrea Lombardi & Ludovico Terzi, 2022. "Multivariate Data-Driven Models for Wind Turbine Power Curves including Sub-Component Temperatures," Energies, MDPI, vol. 16(1), pages 1-18, December.
    4. Pengfei Zhang & Zuoxia Xing & Shanshan Guo & Mingyang Chen & Qingqi Zhao, 2022. "A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging," Energies, MDPI, vol. 15(13), pages 1-15, July.
    5. Guo, Peng & Gan, Yu & Infield, David, 2022. "Wind turbine performance degradation monitoring using DPGMM and Mahalanobis distance," Renewable Energy, Elsevier, vol. 200(C), pages 1-9.
    6. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & Charlie Plumley, 2021. "Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data," Energies, MDPI, vol. 14(20), pages 1-20, October.
    7. Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).

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