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Probabilistic and Risk-Informed Life Extension Assessment of Wind Turbine Structural Components

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  • Jannie Sønderkær Nielsen

    (Department of the Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg, Denmark)

  • Lindsay Miller-Branovacki

    (Environmental Energy Institute, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada)

  • Rupp Carriveau

    (Environmental Energy Institute, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada)

Abstract

Reassessment of the fatigue life for wind turbine structural components is typically performed using deterministic methods with the same partial safety factors as used for the original design. However, in relation to life extension, the conditions are generally different from the assumptions used for calibration of partial safety factors; and using a deterministic assessment method with these partial safety factors might not lead to optimal decisions. In this paper, the deterministic assessment method is compared to probabilistic and risk-based approaches, and the economic feasibility is assessed for a case wind farm. Using the models also used for calibration of partial safety factors in IEC61400-1 ed. 4, it is found that the probabilistic assessment generally leads to longer additional fatigue life than the deterministic assessment method. The longer duration of the extended life can make life extension feasible in more situations. The risk-based model is applied to include the risk of failure directly in the economic feasibility assessment and it is found that the reliability can be much lower than the target for new turbines, without compromising the economic feasibility.

Suggested Citation

  • Jannie Sønderkær Nielsen & Lindsay Miller-Branovacki & Rupp Carriveau, 2021. "Probabilistic and Risk-Informed Life Extension Assessment of Wind Turbine Structural Components," Energies, MDPI, vol. 14(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:821-:d:493416
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    References listed on IDEAS

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    1. Murcia, Juan Pablo & Réthoré, Pierre-Elouan & Dimitrov, Nikolay & Natarajan, Anand & Sørensen, John Dalsgaard & Graf, Peter & Kim, Taeseong, 2018. "Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates," Renewable Energy, Elsevier, vol. 119(C), pages 910-922.
    2. Maria Martinez Luengo & Athanasios Kolios, 2015. "Failure Mode Identification and End of Life Scenarios of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 8(8), pages 1-16, August.
    3. Rubert, T. & Zorzi, G. & Fusiek, G. & Niewczas, P. & McMillan, D. & McAlorum, J. & Perry, M., 2019. "Wind turbine lifetime extension decision-making based on structural health monitoring," Renewable Energy, Elsevier, vol. 143(C), pages 611-621.
    4. Rubert, T. & McMillan, D. & Niewczas, P., 2018. "A decision support tool to assist with lifetime extension of wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 423-433.
    5. Jannie S. Nielsen & John D. Sørensen, 2017. "Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades," Energies, MDPI, vol. 10(5), pages 1-13, May.
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    Cited by:

    1. Jannie Sønderkær Nielsen & Henrik Stensgaard Toft & Gustavo Oliveira Violato, 2023. "Risk-Based Assessment of the Reliability Level for Extreme Limit States in IEC 61400-1," Energies, MDPI, vol. 16(4), pages 1-15, February.

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