Preprints
https://doi.org/10.5194/wes-2024-68
https://doi.org/10.5194/wes-2024-68
28 Jun 2024
 | 28 Jun 2024
Status: this preprint is currently under review for the journal WES.

Probabilistic lifetime extension assessment using mid-term data: Lillgrund wind farm case study

Shadan Mozafari, Jennifer Rinker, Paul Veers, and Katherine Dykes

Abstract. Estimating the site-specific fatigue reliability of wind turbines is an integral part of probabilistic lifetime extension assessment. Limitations in type, accuracy, and availability of site-specific data is one of the main challenges in such estimations. The present research tackles the challenge of estimating long-term fatigue loads using short-term strain gauge measurements via statistical extrapolation. The case study wind turbine is a Siemens 2.3 MW, in the Lillgrund wind farm, located in the Øresund strait between Denmark and Sweden. The turbine is heavily instrumented and Supervisory Control and Data Acquisition (SCADA) is also available. The study also reassesses the performance of the Frandsen model – as a simplified approach for estimating higher turbulence due to wakes – in a compact wind farm layout using aeroelastic simulations of the case study wind turbine. Furthermore, it shows the sensitivity of the site-specific reliability with respect to the uncertainty in material strength, fatigue load, and damage accumulation model.

The results reveal that for the case-study site, the Frandsen model underestimates turbulence in below-rated mean wind speeds and overestimates the turbulence in above-rated mean wind speeds. However, using the Frandsen model for estimating the long-term fatigue loads in the case study location leads to a 35 % lower reliability index than a site-specific assessment using data from the SCADA system and, thus, is relatively more conservative. The study reveals that the sensitivity of the fatigue reliability to the load’s uncertainty is negligible in assessment using site measurements and relatively high when using the Frandsen model.

The extrapolation approach used in the current study can facilitate the use of digital twins when strain gauge measurements are unavailable for a part or the whole span of the lifetime. In addition, the assessment of the Frandsen model in the case study wind farm, as an example of a wind farm with short spacing, adds valuable information to the ongoing studies in the literature about the performance of the model in intense and mixed-waked conditions. Finally, the provided information about robustness of the reliability based on the load estimation approach, is useful for considering uncertainty in the lifetime extension assessment.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Shadan Mozafari, Jennifer Rinker, Paul Veers, and Katherine Dykes

Status: open (until 03 Aug 2024)

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Shadan Mozafari, Jennifer Rinker, Paul Veers, and Katherine Dykes
Shadan Mozafari, Jennifer Rinker, Paul Veers, and Katherine Dykes

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Short summary
The study clarifies the use of probabilistic extrapolation of short/mid-term data for long-term site-specific fatigue assessments. In addition, it assesses the accountability of the Frandsen model in the Lillgrund wind farm as an example of compact layout.
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