| one publication added to basket [311412] | Validation of improved sampling concepts for offshore wind turbine fatigue design
Hubler, C.; Weijtjens, W.; Gebhardt, C.G.; Rolfes, R.; Devriendt, C. (2019). Validation of improved sampling concepts for offshore wind turbine fatigue design. Energies (Basel) 12(4): 603. https://dx.doi.org/10.3390/en12040603 In: Energies (Basel). Molecular Diversity Preservation International (MDPI): Basel. ISSN 1996-1073; e-ISSN 1996-1073, more |   |
| Keyword | | | Author keywords | offshore wind energy; fatigue; uncertainty; monitoring; strainmeasurements; sampling concepts |
| Authors | | Top | - Hubler, C.
- Weijtjens, W., more
- Gebhardt, C.G.
| - Rolfes, R.
- Devriendt, C., more
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| Abstract | Fatigue damage is a design-driving phenomenon for substructures of offshore wind turbines. However, fatigue design based on numerical simulations is quite uncertain. One main reason for this uncertainty is scattering offshore conditions combined with a limited number of simulations (samples). According to current standards, environmental conditions are sampled using a deterministic grid of the most important environmental conditions (e.g., wind speed and direction, significant wave height, and wave period). Recently, there has been some effort to reduce the inherent uncertainty of damage calculations due to limited data by applying other sampling concepts. Still, the investigation of this uncertainty and of methods to reduce it is a subject of ongoing research. In this work, two improved sampling conceptspreviously proposed by the authors and reducing the uncertainty due to limited sampling are validated. The use of strain measurement data enables a realistic estimate of the inherent uncertainty due to limited samples, as numerical effects, etc., are excluded. Furthermore, an extensive data set of three years of data of two turbines of the Belgian wind farm Northwind is available. It is demonstrated that two previously developed sampling methods are generally valid. For a broad range of model types (i.e., input dimensions as well as degrees of non-linearity), they outperform standard sampling concepts such as deterministic grid sampling or Monte Carlo sampling. Hence, they can reduce the uncertainty while keeping the sampling effort constant, or vice versa. |
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