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NREL’s Wind Turbine Drivetrain Condition Monitoring and Wind Plant Operation and Maintenance Research During the 2010s: A US Land-Based Perspective

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Abstract

The wind industry has seen tremendous growth during the past two decades, with the global cumulative installation capacity reaching more than 650 gigawatts by the end of 2019. Despite performance and reliability improvements of utility-scale wind turbines over the years, the industry still experiences premature component failures, leading to increased operation and maintenance (O&M) costs. Among various turbine components, gearboxes—and, more broadly, drivetrains—have shown to be costly to maintain throughout the design life of a wind turbine. The problem of premature component failure is industry wide. As early as 2007, the US Department of Energy (DOE) started to address this challenge through the National Renewable Energy Laboratory’s (NREL’s) reliability initiative that first focused on gearboxes and more recently expanded to entire drivetrains. The wind turbine drivetrain condition monitoring and wind plant O&M research that is the subject of this paper is part of the NREL initiative and includes a few research and development (R&D) activities conducted during the 2010s. These activities included technology evaluation during the first few years; novel monitoring technique investigation (specifically, compact filter analysis) during the middle years; and data and physics domain modeling for fault detection and prediction in recent years. A high-level summary of these activities is provided in this paper along with some key observations from each activity. Most of the work discussed has been published and can be referred to for more information. They reflect the expected evolution of wind turbine condition monitoring and O&M in the US market—primarily, a land-based perspective. In addition, we have identified several R&D opportunities that can be picked up by the research community to help industry advance in related areas, making wind power more cost competitive in the future.

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References

  1. GWEC. Global wind report 2019. https://gwec.net/global-wind-report-2019/. Accessed on 23 Oct 2020

  2. Musial, M., Butterfield, S., McNiff, B.: Improving Wind Turbine Gearbox Reliability. United States. https://www.osti.gov/servlets/purl/909663. Accessed on 23 Oct 2020

  3. Link, H., LaCava, W., van Dam, J., McNiff, B., Sheng, S., Wallen, R., McDade, M., Lambert, S., Butterfield, S., Oyague, F.: Gearbox reliability collaborative project report: findings from phase 1 and phase 2 testing. National Renewable Energy Laboratory, Golden (2011)

    Book  Google Scholar 

  4. Keller, J., Sheng, S., Cotrell, J., Greco, A.: Wind turbine drivetrain reliability collaborative workshop: a recap. In. (2016)

  5. Sheng, S., Oyague, F., Butterfield, S.: Investigation of various wind turbine drive train condition monitoring techniques. In: Paper presented at the International Workshop on Structural Health Monitoring, Stanford, California, September 9–11 (2009)

  6. Sheng, S.: Investigation of various condition monitoring techniques based on a damaged wind turbine gearbox. In: Paper Presented at the International Workshop on Structural Health Monitoring, Stanford, California, September 13–15 (2011)

  7. Sheng, S.: Monitoring of wind turbine gearbox condition through oil and wear debris analysis: a full-scale testing perspective. Tribol Trans 59(1), 149–162 (2016). https://doi.org/10.1080/10402004.2015.1055621

    Article  Google Scholar 

  8. Orozco, R., Sheng, S., Phillips, C.: Diagnostic models for wind turbine gearbox components using scada time series data. In: 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 11–13 June 2018, pp. 1–9 (2018)

  9. Guo, Y., Sheng, S., Phillips, C., Keller, J., Veers, P., Williams, L.: A methodology for reliability assessment and prognosis of bearing axial cracking in wind turbine gearboxes. Renew. Sustain. Energy Rev. 127, 109888 (2020). https://doi.org/10.1016/j.rser.2020.109888

    Article  Google Scholar 

  10. Sheng, S., Hal, L., William, L., van Dam, J., McNiff, B., Veers, P., Keller, J., Butterfield, S., Oyague, F.: Wind turbine drivetrain condition monitoring during gearbox reliability collaborative (GRC) phase 1 and phase 2 testing. National Renewable Energy Laboratory, Golden (2011)

    Book  Google Scholar 

  11. Sheng, S.: Wind Turbine Gearbox Condition Monitoring Round Robin Study—Vibration Analysis. National Renewable Energy Laboratory, Golden (2012)

    Book  Google Scholar 

  12. Sheng, S.: Wind turbine condition monitoring. Wind Energy 17(5), 671–672 (2014). https://doi.org/10.1002/we.1725

    Article  Google Scholar 

  13. Sheng, S., Herguth, W, Roberts, D.: Condition monitoring of wind turbine gearboxes through compact filter element analysis. In: Presented at the 2013 Society of Tribologists and Lubrication Engineers Annual Meeting and Exhibition, Detroit, MI, May 6–9 (2013)

  14. Sheng, S., Roberts, D.: Improving the analysis of gear-oil debris with a compact filter. Windpower Eng. Dev. 9, 44–46 (2017)

    Google Scholar 

  15. Sheng, S., Guo, Y.: A prognostics and health management framework for wind. In: ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition 2019, V009T48A013 (2019)

  16. Kim, K., Parthasarathy, G., Uluyol, O., Foslien, W., Sheng, S., Fleming, P.: Use of SCADA data for failure detection in wind turbines. In: ASME 2011 5th International Conference on Energy Sustainability 2011, pp. 2071–2079 (2011)

  17. Yampikulsakul, N., Eunshin, B., Shuai, H., Shuangwen, S., Mingdi, Y.: Condition monitoring of wind power system with nonparametric regression analysis. Energy conversion. IEEE Trans. 29(2), 288–299 (2014). https://doi.org/10.1109/TEC.2013.2295301

    Article  Google Scholar 

  18. Williams, L., Phillips, C., Sheng, S., Dobos, A., Wei, X.: Scalable wind turbine generator bearing fault prediction using machine learning: a case study. In: 2020 IEEE International Conference on Prognostics and Health Management (ICPHM), Detroit, MI, 2020, pp. 1–9 (2020). doi: https://doi.org/10.1109/ICPHM49022.2020.9187050

  19. Desai, A., Guo, Y., Sheng, S., Phillips, C., Williams, L. (2020) Prognosis of wind turbine gearbox bearing failures using SCADA and modeled data. PHM_CONF 12(1):10

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Acknowledgements

Contributions to the NREL condition monitoring and wind plant O&M research by other team members—especially Dr. Yi Guo, Lindy Williams, and Dr. Jon Keller—are greatly appreciated. Without the support from partners in both academia and industry, the reported work would not have been possible. This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding was provided by the US Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the US Government. The US Government retains, and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US Government purposes.

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Correspondence to Shawn Sheng.

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Sheng, S. NREL’s Wind Turbine Drivetrain Condition Monitoring and Wind Plant Operation and Maintenance Research During the 2010s: A US Land-Based Perspective. Acoust Aust 49, 239–249 (2021). https://doi.org/10.1007/s40857-021-00223-8

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