IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i4p915-d496494.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/4/915/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/4/915/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ravi Pandit & David Infield, 2018. "Gaussian Process Operational Curves for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 11(7), pages 1-20, June.
    2. 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.
    3. Kim, Dae-Young & Kim, Yeon-Hee & Kim, Bum-Suk, 2021. "Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear," Energy, Elsevier, vol. 214(C).
    4. Bahamonde, Manuel Ignacio & Litrán, Salvador P., 2019. "Study of the energy production of a wind turbine in the open sea considering the continuous variations of the atmospheric stability and the sea surface roughness," Renewable Energy, Elsevier, vol. 135(C), pages 163-175.
    5. Sequeira, C. & Pacheco, A. & Galego, P. & Gorbeña, E., 2019. "Analysis of the efficiency of wind turbine gearboxes using the temperature variable," Renewable Energy, Elsevier, vol. 135(C), pages 465-472.
    6. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    7. Davide Astolfi & Francesco Castellani & Ludovico Terzi, 2018. "Wind Turbine Power Curve Upgrades," Energies, MDPI, vol. 11(5), pages 1-17, May.
    8. 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.
    9. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    3. 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.
    4. 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.
    5. 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.
    6. Davide Astolfi & Ravi Pandit, 2022. "Wind Turbine Performance Decline with Age," Energies, MDPI, vol. 15(14), pages 1-4, July.
    7. 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.
    8. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    9. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Suo Li & Ling-ling Huang & Yang Liu & Meng-yao Zhang, 2021. "Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines," Energies, MDPI, vol. 14(4), pages 1-16, February.
    11. Jäger, Tobias & McKenna, Russell & Fichtner, Wolf, 2015. "Onshore wind energy in Baden-Württemberg: a bottom-up economic assessment of the socio-technical potential," Working Paper Series in Production and Energy 7, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    12. Francisco Haces-Fernandez, 2020. "GoWInD: Wind Energy Spatiotemporal Assessment and Characterization of End-of-Life Activities," Energies, MDPI, vol. 13(22), pages 1-20, November.
    13. Abdollahzadeh, Hadi & Atashgar, Karim & Abbasi, Morteza, 2016. "Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups," Renewable Energy, Elsevier, vol. 88(C), pages 247-261.
    14. Niklas Andersen & Ola Eriksson & Karl Hillman & Marita Wallhagen, 2016. "Wind Turbines’ End-of-Life: Quantification and Characterisation of Future Waste Materials on a National Level," Energies, MDPI, vol. 9(12), pages 1-24, November.
    15. Abadie, Luis Mª & Chamorro, José M., 2023. "Investment in wind-based hydrogen production under economic and physical uncertainties," Applied Energy, Elsevier, vol. 337(C).
    16. Hayashi, Daisuke & Huenteler, Joern & Lewis, Joanna I., 2018. "Gone with the wind: A learning curve analysis of China's wind power industry," Energy Policy, Elsevier, vol. 120(C), pages 38-51.
    17. Ramirez Camargo, Luis & Gruber, Katharina & Nitsch, Felix, 2019. "Assessing variables of regional reanalysis data sets relevant for modelling small-scale renewable energy systems," Renewable Energy, Elsevier, vol. 133(C), pages 1468-1478.
    18. Cannon, D.J. & Brayshaw, D.J. & Methven, J. & Coker, P.J. & Lenaghan, D., 2015. "Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain," Renewable Energy, Elsevier, vol. 75(C), pages 767-778.
    19. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2021. "Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes," Energy, Elsevier, vol. 218(C).
    20. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:915-:d:496494. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.