Design clustering of offshore wind turbines using probabilistic fatigue load estimation

Lisa Ziegler, Sven Voormeeren, Sebastian Schafhirt and Michael Muskulus

Renewable Energy, 2016, vol. 91, issue C, 425-433

Abstract: In large offshore wind farms fatigue loads on support structures can vary significantly due to differences and uncertainties in site conditions, making it necessary to optimize design clustering. An efficient probabilistic fatigue load estimation method for monopile foundations was implemented using Monte-Carlo simulations. Verification of frequency domain analysis for wave loads and scaling approaches for wind loads with time domain aero-elastic simulations lead to 95% accuracy on equivalent bending moments at mudline and tower bottom. The computational speed is in the order of 100 times faster than typical time domain tools. The model is applied to calculate location specific fatigue loads that can be used in deterministic and probabilistic design clustering. Results for an example wind farm with 150 turbines in 30–40 m water depth show a maximum load difference of 25%. Smart clustering using discrete optimization algorithms leads to a design load reduction of up to 13% compared to designs based on only the highest loaded turbine position. The proposed tool improves industry-standard clustering and provides a basis for design optimization and uncertainty analysis in large wind farms.

Keywords: Offshore wind turbine; Fatigue load; Clustering; Uncertainty; Optimization; Frequency domain (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:91:y:2016:i:c:p:425-433

DOI: 10.1016/j.renene.2016.01.033

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