Description of an 8 MW reference wind turbine

Desmond, Cian; Murphy, Jerry D.; Blonk, Lindert; Haans, Wouter

doi:10.1088/1742-6596/753/9/092013

Cited by 82 publications

(49 citation statements)

References 2 publications

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“…The methodology presented above was applied to larger size wind turbines. The turbine sizes selected for this study were the Vestas V164 8.0 MW wind turbine and the DTU reference 10 MW wind turbine described in (Desmond, et al, 2016). The wind turbines were placed in different windfarm sites shown in Table 4 and accordingly the loading ratio was computed.…”

Section: Mmin/mmax For Operating Wind Turbinesmentioning

confidence: 99%

A method to predict the cyclic loading profiles (one-way or two-way) for monopile supported offshore wind turbines

et al. 2019

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Section: Mmin/mmax For Operating Wind Turbinesmentioning

confidence: 99%

A method to predict the cyclic loading profiles (one-way or two-way) for monopile supported offshore wind turbines

et al. 2019

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“…In terms of the energy yield calculation, the 8MW wind turbine developed in EU FP7's project, LEANWIND [29], with a 110 m hub height, was chosen. Accordingly, the wind data at the given height was obtained from the database of the Global Wind Atlas 3.0 [30], and that of the waves was obtained from the Danish Hydraulics Institute (DHI) [31].…”

Section: General Descriptionmentioning

confidence: 99%

Layout Optimization Process to Minimize the Cost of Energy of an Offshore Floating Hybrid Wind–Wave Farm

et al. 2020

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Offshore floating hybrid wind and wave energy is a young technology yet to be scaled up. A way to reduce the total costs of the energy production process in order to ensure competitiveness in the sustainable energy market is to maximize the farm’s efficiency. To do so, an energy generation and costs calculation model was developed with the objective of minimizing the technology’s Levelized Cost of Energy (LCOE) of the P80 hybrid wind-wave concept, designed by the company Floating Power Plant A/S. A Particle Swarm Optimization (PSO) algorithm was then implemented on top of other technical and decision-making processes, taking as decision variables the layout, the offshore substation position, and the export cable choice. The process was applied off the west coast of Ireland in a site of interest for the company, and after a quantitative and qualitative optimization process, a minimized LCOE was obtained. It was then found that lower costs of ~73% can be reached in the short-term, and the room for improvement in the structure’s design and materials was highlighted, with an LCOE reduction potential of up to 32%. The model serves usefully as a preliminary analysis. However, the uncertainty estimate of 11% indicates that further site-specific studies and measurements are essential.

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“…The initial rationale behind this turbine was to set a reference size turbine to enable OWT studies. DTU would later introduce a 10‐MW reference turbine, and the EU LEANWIND project would promote an 8‐MW reference turbine . Nonetheless, regarding the progressive availability of information and incidence on the research field, NREL's 5MW OWT has captivated significant pertinence.…”

Section: Owt Sn Fatigue Designmentioning

confidence: 99%

“…DTU would later introduce a 10-MW reference turbine, 46 and the EU LEANWIND project would promote an 8-MW reference turbine. 47 Nonetheless, regarding the progressive availability of information and incidence on the research field, NREL's 5MW OWT has captivated significant pertinence. It has been the reference turbine for offshore code comparison studies OC3, OC4, and more recently OC5.…”

Section: Owt Simulationmentioning

confidence: 99%

Analysis of long‐term loading characterization for stress‐cycle fatigue design

2019

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The present paper discusses the application of stress‐cycle (SN) fatigue design procedures for offshore wind turbines, applied to the tower and substructure, and emphasizing the long‐term loading characterization. Three main methodologies are compared to assess SN fatigue long‐term design, direct scale‐up of loads and cycles to the design lifetime, probability fit for the loading distribution, and truncation and fit of the tail region. Different characteristic SN slopes are researched. The comprehensive comparison developed shows that limited interest exists in increasing the complexity of the loading assessment for SN fatigue design. In particular, the notion of representative sample has key influence on the fatigue calculations. Probability analysis techniques are not an adequate substitute of a representative sample. If definition of a representative sample for design is unpractical due to its large cost, direct scale‐up of cycles from a time t to a larger time T can be applied with uncertainty assessment. Bootstrapping of load ranges is implemented to tackle the limitations imposed by approaching a design time much larger than the evaluated time. It takes advantage of the fact that SN fatigue design is a statistical problem of mean value. Bootstrapping proved to be an efficient estimator of uncertainty with relatively constant confidence bounds, of particular interest to perform SN fatigue design with small samples. New insights on its application for fatigue design are presented. Due to its simple application and nonparametric character, it can be applied in combination with other techniques, such as extrapolation of loads and cycles.

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Description of an 8 MW reference wind turbine

Cited by 82 publications

References 2 publications

A method to predict the cyclic loading profiles (one-way or two-way) for monopile supported offshore wind turbines

A method to predict the cyclic loading profiles (one-way or two-way) for monopile supported offshore wind turbines

Layout Optimization Process to Minimize the Cost of Energy of an Offshore Floating Hybrid Wind–Wave Farm

Analysis of long‐term loading characterization for stress‐cycle fatigue design

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