Investigation of Site-Specific Wind Field Parameters and Their Effect on Loads of Offshore Wind Turbines

Ernst, Benedikt; Seume, Joerg R.

doi:10.3390/en5103835

Cited by 45 publications

(29 citation statements)

References 25 publications

(66 reference statements)

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“…It is an offshore platform in the North Sea . It consists of a 100‐m tall meteorological tower equipped with wind speed measurement sensors (cup anemometers) at heights of 33, 40, 50, 60, 70, 80, 90, and 100 m. A total of 91 months of wind speed data collected over a period of 9 years (2004‐2012) are used in the present study.…”

Section: Description Of Wind Datasetsmentioning

confidence: 99%

On the tails of the wind ramp distributions

DeMarco

Basu

2018

Wind Energy

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We analyzed several multiyear wind speed datasets from 4 different geographical locations. The probability density functions of wind ramps from all these sites revealed remarkably similar shapes. The tails of the probability density functions are much heavier than a Gaussian distribution, and they also systematically depend on time increments. Quite interestingly, from a purely statistical standpoint, the characteristics of the extreme ramp‐up and ramp‐down events are found to be almost identical. With the aid of extreme value theory, we describe several other inherent features of extreme wind ramps in this paper.

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Section: Description Of Wind Datasetsmentioning

confidence: 99%

On the tails of the wind ramp distributions

DeMarco

Basu

2018

Wind Energy

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“…FINO 1 is an offshore platform in the North Sea [25][26][27] Over the past four decades, observational data from the Cabauw (the Netherlands) meteorological tower have been used in various ABL studies [28][29][30][31] All the aforementioned datasets contain variable amount of missing data. This data-loss problem is more severe for the sodars.…”

Section: Description Of Sites and Datasetsmentioning

confidence: 99%

Buoyancy effects on the scaling characteristics of atmospheric boundary-layer wind fields in the mesoscale range

Kiliyanpilakkil¹,

Basu²,

Ruiz-Columbie³

et al. 2015

Phys. Rev. E

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We have analyzed long-term wind speed time-series from five field sites up to a height of 300 m from the ground. Structure function-based scaling analysis has revealed that the scaling exponents in the mesoscale regime systematically depend on height. This anomalous behavior is likely caused by the buoyancy effects. In the framework of the extended self-similarity, the relative scaling exponents portray quasi-universal behavior.

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“…Among the probabilistic models available in the literature, the conditional joint distribution model is the most popular and adopted in the design code [8,31,17]. A common example, is the joint distribution of significant wave height (H S ) and peak period (T P ), which characterizes the occurrences of sea states.…”

Section: Conditional Joint Distributionmentioning

confidence: 99%