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Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis

Author

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  • Davide Astolfi

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

  • Raymond Byrne

    (Centre for Renewables and Energy-Dundalk Institute of Technology, Dublin Road, A91 V5XR Louth, Ireland)

  • Francesco Castellani

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

Abstract

It is a common sense expectation that the efficiency of wind turbines should decline with age, similarly to what happens with most technical systems. Due to the complexity of this kind of machine and the environmental conditions to which it is subjected, it is far from obvious how to reliably estimate the impact of aging. In this work, the aging of five Vestas V52 wind turbines is analyzed. The test cases belong to two different sites: one is at the Dundalk Institute of Technology in Ireland, and four are sited in an industrial wind farm in a mountainous area in Italy. Innovative data analysis techniques are employed: the general idea consists of considering appropriate operation curves depending on the working control region of the wind turbines. When the wind turbine operates at fixed pitch and variable rotational speed, the generator speed-power curve is studied; for higher wind speed, when the rotational speed has saturated and the blade pitch is variable, the blade pitch-power curve is considered. The operation curves of interest are studied through the binning method and through a support vector regression with a Gaussian kernel. The wind turbine test cases are analyzed vertically (each in its own history) and horizontally, by comparing the behavior at the two sites for the given wind turbine age. The main result of this study is that an evident effect of aging is the worsening of generator efficiency: progressively, less power is extracted for the given generator rotational speed. Nevertheless, this effect is observed to be lower for the wind turbines in Italy (order of −1.5% at 12 years of age with respect to seven years of age) with respect to the Dundalk wind turbine, which shows a sharp decline at 12 years of age (−8.8%). One wind turbine sited in Italy underwent a generator replacement in 2018: through the use of the same kind of data analysis methods, it was possible to observe that an average performance recovery of the order of 2% occurs after the component replacement. It also arises that for all the test cases, a slight aging effect is visible for higher wind speed, which can likely be interpreted as due to declining gearbox efficiency. In general, it is confirmed that the aging of wind turbines is strongly dependent on the history of each machine, and it is likely confirmed that the technology development mitigates the effect of aging.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:915-:d:496494
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    References listed on IDEAS

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    1. Rebecca J. Barthelmie & Tristan J. Shepherd & Jeanie A. Aird & Sara C. Pryor, 2020. "Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains," Energies, MDPI, vol. 13(16), pages 1-21, August.
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    Cited by:

    1. 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.
    2. Altaf Hussain Rajpar & Imran Ali & Ahmad E. Eladwi & Mohamed Bashir Ali Bashir, 2021. "Recent Development in the Design of Wind Deflectors for Vertical Axis Wind Turbine: A Review," Energies, MDPI, vol. 14(16), pages 1-23, August.
    3. 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.
    4. 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.
    5. Erik Möllerström & Sean Gregory & Aromal Sugathan, 2021. "Improvement of AEP Predictions with Time for Swedish Wind Farms," Energies, MDPI, vol. 14(12), pages 1-12, June.
    6. Benjamin Pakenham & Anna Ermakova & Ali Mehmanparast, 2021. "A Review of Life Extension Strategies for Offshore Wind Farms Using Techno-Economic Assessments," Energies, MDPI, vol. 14(7), pages 1-23, March.
    7. Davide Astolfi & Ravi Pandit, 2022. "Wind Turbine Performance Decline with Age," Energies, MDPI, vol. 15(14), pages 1-4, July.
    8. Benini, Giacomo & Cattani, Gilles, 2022. "Measuring the long run technical efficiency of offshore wind farms," Applied Energy, Elsevier, vol. 308(C).

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