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Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis

Author

Listed:
  • Davide Astolfi

    (Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy)

  • Ravi Pandit

    (Centre for Life-Cycle Engineering and Management (CLEM), School of Aerospace Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK)

  • Ludovico Terzi

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

  • Andrea Lombardi

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

Abstract

This work is devoted to the formulation of innovative SCADA-based methods for wind turbine performance analysis and interpretation. The work is organized as an academia–industry collaboration: three test cases are analyzed, two with hydraulic pitch control (Vestas V90 and V100) and one with electric pitch control (Senvion MM92). The investigation is based on the method of bins, on a polynomial regression applied to operation curves that have never been analyzed in detail in the literature before, and on correlation and causality analysis. A key point is the analysis of measurement channels related to the blade pitch control and to the rotor: pitch manifold pressure, pitch piston traveled distance and tower vibrations for the hydraulic pitch wind turbines, and blade pitch current for the electric pitch wind turbines. The main result of this study is that cases of noticeable under-performance are observed for the hydraulic pitch wind turbines, which are associated with pitch pressure decrease in time for one case and to suspected rotor unbalance for another case. On the other way round, the behavior of the rotational speed and blade pitch curves is homogeneous and stable for the wind turbines electrically controlled. Summarizing, the evidence collected in this work identifies the hydraulic pitch as a sensible component of the wind turbine that should be monitored cautiously because it is likely associated with performance decline with age.

Suggested Citation

  • Davide Astolfi & Ravi Pandit & Ludovico Terzi & Andrea Lombardi, 2022. "Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis," Energies, MDPI, vol. 15(15), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5343-:d:869669
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    References listed on IDEAS

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    4. Benini, Giacomo & Cattani, Gilles, 2022. "Measuring the long run technical efficiency of offshore wind farms," Applied Energy, Elsevier, vol. 308(C).
    5. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2021. "Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis," Energies, MDPI, vol. 14(4), pages 1-25, February.
    6. Davide Astolfi & Ravi Pandit, 2022. "Wind Turbine Performance Decline with Age," Energies, MDPI, vol. 15(14), pages 1-4, July.
    7. John Thomas Lyons & Tuhfe Göçmen, 2021. "Applied Machine Learning Techniques for Performance Analysis in Large Wind Farms," Energies, MDPI, vol. 14(13), pages 1-28, June.
    8. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    9. Hyun-Goo Kim & Jin-Young Kim, 2021. "Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement," Energies, MDPI, vol. 14(8), pages 1-12, April.
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    11. Raymond Byrne & Davide Astolfi & Francesco Castellani & Neil J. Hewitt, 2020. "A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    12. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
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    Cited by:

    1. Zhan, Jun & Wu, Chengkun & Yang, Canqun & Miao, Qiucheng & Wang, Shilin & Ma, Xiandong, 2022. "Condition monitoring of wind turbines based on spatial-temporal feature aggregation networks," Renewable Energy, Elsevier, vol. 200(C), pages 751-766.
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    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. Francesco Castellani & Ravi Pandit & Francesco Natili & Francesca Belcastro & Davide Astolfi, 2023. "Advanced Methods for Wind Turbine Performance Analysis Based on SCADA Data and CFD Simulations," Energies, MDPI, vol. 16(3), pages 1-15, January.
    5. Davide Astolfi, 2023. "Wind Turbine Drivetrain Condition Monitoring through SCADA-Collected Temperature Data: Discussion of Selected Recent Papers," Energies, MDPI, vol. 16(9), pages 1-4, April.
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