Articles | Volume 9, issue 4
https://doi.org/10.5194/wes-9-799-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-799-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sensitivity of fatigue reliability in wind turbines: effects of design turbulence and the Wöhler exponent
Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark
Paul Veers
National Renewable Energy Laboratory (NREL), Golden, CO, USA
Jennifer Rinker
Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark
Katherine Dykes
Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark
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Revised manuscript under review for WES
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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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We developed and tested three methods to estimate wind speed variations across the entire rotor area of a wind turbine using lidar data. Unlike traditional approaches that focus on average wind speed, our methods capture detailed inflow structures. This allows the turbine to anticipate changes, improving control and reducing wear – provided the estimation settings are properly selected.
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Offshore wind turbines are prone to fatigue caused by loads from wind, waves, and operation. It may be possible to extend their life by monitoring stress histories. However, this is challenging, as part of the structure is sub-sea and sub-soil. Model-based virtual sensing offers a solution, however, current models simplify the rotor, which can lead to errors. This work addresses this error and concludes that an improved rotor model must be implemented to improve the stress history estimates.
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Most human activity happens in the layer of the atmosphere which extends a few hundred meters to a couple of kilometers above the surface of the Earth. The flow in this layer is turbulent. Turbulence impacts wind power production and turbine lifespan. Optimizing wind turbine performance requires understanding how turbulence affects both wind turbine efficiency and reliability. This paper points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.
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Revised manuscript under review for WES
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Social aspects are gaining traction in wind energy. A recent publication by Kirkegaard et al. lays out social grand challenges. We discuss them for a more technologically focused audience. We describe the role of social sciences in wind energy research, showing insights, topics, and value-added for public engagement and planning, just ownership and value-based design. We reflect how social and technical sciences can jointly advance wind energy research into a new interdisciplinary era.
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We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
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Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
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We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
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Short summary
Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how to model the turbulence in normal wind conditions in the design phase. To address such debates, we study the fatigue load distribution and reliability following different models of the International Electrotechnical Commission 61400-1 standard. The results show the lesser importance of load uncertainty due to turbulence distribution compared to the uncertainty of material resistance and Miner’s rule.
Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how...
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