Simulation of Wave Impacts at Belwind Offshore Wind Farm and Comparison With Full-Scale Measurements

Author(s):  
Tim Bunnik ◽  
Wout Weijtjens ◽  
Christof Devriendt

The effects of operational wave loads and wind loads on offshore monopile wind turbines are well understood. For most sites, however, the water depth is such that steep and/or breaking waves will occur causing impulsive excitation of the monopile and consequently considerable stresses, displacements and accelerations in the monopile, tower and turbine. At Belwind offshore wind farm (offshore Zeebrugge, Belgium) the waves and accelerations of a Vestas V90 3MW wind turbine have been monitored since November 2013, using wave radar and several accelerometers. During this period the wind turbine was exposed to several storms and experienced several wave impacts, resulting in vibrations in the monopile. The measurements were compared with results from a numerical model for the flexible response of wind turbines due to steep waves. Previously this model was compared with scale model tests with satisfying results. The full-scale measurements provide an additional cross-check of the model. The numerical model consists of a one-way coupling between a CFD model for wave loads and a simplified structural model based on mode shapes. An iterative wave calibration technique has been developed in the CFD model to ensure a good match between the simulated and measured incoming wave profile, obtained with the wave radar. This makes a deterministic comparison between simulations and measurements possible. This iteration is carried out in a 2D CFD domain (assuming long-crested waves) and is therefore relatively cheap. The calibrated numerical wave is then simulated in a 3D CFD domain including a (fixed) wind turbine. The resulting wave pressures on the turbine have been used to compute the modal excitation and subsequently the modal response of the wind turbine. The mode shapes have been estimated from the measured accelerations at the Belwind turbine. A grid refinement study was done to verify the results from the numerical model. The horizontal accelerations resulting from this one-way coupling are in fair agreement with the measured accelerations.

Author(s):  
Bryan Nelson ◽  
Yann Quéméner

This study evaluated, by time-domain simulations, the fatigue lives of several jacket support structures for 4 MW wind turbines distributed throughout an offshore wind farm off Taiwan’s west coast. An in-house RANS-based wind farm analysis tool, WiFa3D, has been developed to determine the effects of the wind turbine wake behaviour on the flow fields through wind farm clusters. To reduce computational cost, WiFa3D employs actuator disk models to simulate the body forces imposed on the flow field by the target wind turbines, where the actuator disk is defined by the swept region of the rotor in space, and a body force distribution representing the aerodynamic characteristics of the rotor is assigned within this virtual disk. Simulations were performed for a range of environmental conditions, which were then combined with preliminary site survey metocean data to produce a long-term statistical environment. The short-term environmental loads on the wind turbine rotors were calculated by an unsteady blade element momentum (BEM) model of the target 4 MW wind turbines. The fatigue assessment of the jacket support structure was then conducted by applying the Rainflow Counting scheme on the hot spot stresses variations, as read-out from Finite Element results, and by employing appropriate SN curves. The fatigue lives of several wind turbine support structures taken at various locations in the wind farm showed significant variations with the preliminary design condition that assumed a single wind turbine without wake disturbance from other units.


2021 ◽  
Vol 6 (4) ◽  
pp. 997-1014
Author(s):  
Janna Kristina Seifert ◽  
Martin Kraft ◽  
Martin Kühn ◽  
Laura J. Lukassen

Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.


2015 ◽  
Vol 74 ◽  
pp. 406-413 ◽  
Author(s):  
Wei Shi ◽  
Jonghoon Han ◽  
Changwan Kim ◽  
Daeyong Lee ◽  
Hyunkyoung Shin ◽  
...  

2016 ◽  
Author(s):  
Amy Stidworthy ◽  
David Carruthers

Abstract. A new model, FLOWSTAR-Energy, has been developed for the practical calculation of wind farm energy production. It includes a semi-analytic model for airflow over complex surfaces (FLOWSTAR) and a wind turbine wake model that simulates wake-wake interaction by exploiting some similarities between the decay of a wind turbine wake and the dispersion of plume of passive gas emitted from an elevated source. Additional turbulence due to the wind shear at the wake edge is included and the assumption is made that wind turbines are only affected by wakes from upstream wind turbines. The model takes account of the structure of the atmospheric boundary layer, which means that the effect of atmospheric stability is included. A marine boundary layer scheme is also included to enable offshore as well as onshore sites to be modelled. FLOWSTAR-Energy has been used to model three different wind farms and the predicted energy output compared with measured data. Maps of wind speed and turbulence have also been calculated for two of the wind farms. The Tjaæreborg wind farm is an onshore site consisting of a single 2 MW wind turbine, the NoordZee offshore wind farm consists of 36 V90 VESTAS 3 MW turbines and the Nysted offshore wind farm consists of 72 Bonus 2.3 MW turbines. The NoordZee and Nysted measurement datasets include stability distribution data, which was included in the modelling. Of the two offshore wind farm datasets, the Noordzee dataset focuses on a single 5-degree wind direction sector and therefore only represents a limited number of measurements (1,284); whereas the Nysted dataset captures data for seven 5-degree wind direction sectors and represents a larger number of measurements (84,363). The best agreement between modelled and measured data was obtained with the Nysted dataset, with high correlation (0.98 or above) and low normalised mean square error (0.007 or below) for all three flow cases. The results from Tjæreborg show that the model replicates the Gaussian shape of the wake deficit two turbine diameters downstream of the turbine, but the lack of stability information in this dataset makes it difficult to draw conclusions about model performance. One of the key strengths of FLOWSTAR-Energy is its ability to model the effects of complex terrain on the airflow. However, although the airflow model has been previously compared extensively with flow data, it has so far not been used in detail to predict energy yields from wind farms in complex terrain. This will be the subject of a further validation study for FLOWSTAR-Energy.


2001 ◽  
Vol 123 (4) ◽  
pp. 296-303 ◽  
Author(s):  
Peter Fuglsang ◽  
Kenneth Thomsen

A method is presented for site-specific design of wind turbines where cost of energy is minimized. A numerical optimization algorithm was used together with an aeroelastic load prediction code and a cost model. The wind climate was modeled in detail including simulated turbulence. Response time series were calculated for relevant load cases, and lifetime equivalent fatigue loads were derived. For the fatigue loads, an intelligent sensitivity analysis was used to reduce computational costs. Extreme loads were derived from statistical response calculations of the Davenport type. A comparison of a 1.5 MW stall regulated wind turbine in normal onshore flat terrain and in an offshore wind farm showed a potential increase in energy production of 28% for the offshore wind farm, but also significant increases in most fatigue loads and in cost of energy. Overall design variables were optimized for both sites. Compared to an onshore optimization, the offshore optimization increased swept area and rated power whereas hub height was reduced. Cost of energy from manufacture and installation for the offshore site was reduced by 10.6% to 4.6¢. This reduction makes offshore wind power competitive compared with today’s onshore wind turbines. The presented study was made for one wind turbine concept only, and many of the involved sub models were based on simplified assumptions. Thus there is a need for further studies of these models.


2021 ◽  
Author(s):  
Morteza Bahadori ◽  
Hassan Ghassemi

Abstract In recent years, as more offshore wind farms have been constructed, the possibility of integrating various offshore renewable technologies is increased. Using offshore wind and solar power resources as a hybrid system provides several advantages including optimized marine space utilization, reduced maintenance and operation costs, and relieving wind variability on output power. In this research, both offshore wind and solar resources are analyzed based on accurate data through a case study in Shark Bay (Australia), where bathymetric information confirms using offshore bottom-fixed wind turbine regarding the depth of water. Also, the power production of the hybrid system of co-located bottom-fixed wind turbine and floating photovoltaic are investigated with the technical characteristics of commercial mono-pile wind turbine and photovoltaic panels. Despite the offshore wind, the solar energy output has negligible variation across the case study area, therefore using the solar platform in deep water is not an efficient option. It is demonstrated that the floating solar has a power production rate nearly six times more than a typical offshore wind farm with the same occupied area. Also, output energy and surface power density of the hybrid offshore windsolar system are improved significantly compared to a standalone offshore wind farm. The benefits of offshore wind and solar synergies augment the efficiency of current offshore wind farms throughout the world.


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