HYBRID WAKE MODEL BASED ON ANALYTICAL MULTI-WAKE MODEL AND CFD MODEL FOR OFFSHORE WIND FARM

FLOWSTAR-Energy: a high resolution wind farm wake model

10.5194/wes-2016-34 ◽

2016 ◽

Author(s):

Amy Stidworthy ◽

David Carruthers

Keyword(s):

Boundary Layer ◽

Wind Turbine ◽

Complex Terrain ◽

Wind Farm ◽

Wind Farms ◽

Measured Data ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Wake Model ◽

Turbine Wake

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.

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Monitoring offshore wind farm power performance with SCADA data and an advanced wake model

Wind Energy Science ◽

10.5194/wes-2-175-2017 ◽

2017 ◽

Vol 2 (1) ◽

pp. 175-187 ◽

Cited By ~ 8

Author(s):

Niko Mittelmeier ◽

Tomas Blodau ◽

Martin Kühn

Keyword(s):

Power Output ◽

Wind Farm ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Power Performance ◽

Power Normalization ◽

Efficient Detection ◽

Asset Value ◽

Output Ratio ◽

Wake Model

Abstract. Wind farm underperformance can lead to significant losses in revenues. The efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method, presented in this paper, estimates the environmental conditions from turbine states and uses pre-calculated lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output ratio between two turbines are an indication of underperformance. The confidence of detected underperformance is estimated by a detailed analysis of the uncertainties of the method. Power normalization with reference turbines and averaging several measures performed by devices of the same type can reduce uncertainties for estimating the expected power. A demonstration of the method's ability to detect underperformance in the form of degradation and curtailment is given. An underperformance of 8 % could be detected in a triple-wake condition.

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Monitoring offshore wind farm power performance with SCADA data and advanced wake model

10.5194/wes-2016-16 ◽

2016 ◽

Cited By ~ 1

Author(s):

Niko Mittelmeier ◽

Tomas Blodau ◽

Martin Kühn

Keyword(s):

Power Output ◽

Wind Farm ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Power Performance ◽

Efficient Detection ◽

Asset Value ◽

Measurement Devices ◽

Wake Model ◽

Measured Power

Abstract. Wind farm underperformance can lead to significant losses in revenues. Efficient detection of wind turbines operating below their expected power output and immediate corrections help maximise asset value. The presented method estimates the environmental conditions from turbine states and uses pre-calculated power matrices from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output are an indication of underperformance. The confidence of detected underperformance is estimated by detailed analysis of uncertainties of the method. Power normalisation with reference turbines and averaging several measurement devices can reduce uncertainties for estimating the expected power. A demonstration of the method’s ability to detect underperformance in the form of degradation and curtailment is given. Underperformance of 8 % could be detected in a triple wake condition.

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

Volume 10: Ocean Renewable Energy ◽

10.1115/omae2017-61305 ◽

2017 ◽

Author(s):

Tim Bunnik ◽

Wout Weijtjens ◽

Christof Devriendt

Keyword(s):

Numerical Model ◽

Wind Turbine ◽

Wind Farm ◽

Full Scale ◽

Offshore Wind ◽

Mode Shapes ◽

Wave Loads ◽

Cfd Model ◽

Offshore Wind Farm ◽

Wave Radar

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.

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Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

10.5194/wes-2018-62 ◽

2018 ◽

Author(s):

Thomas Duc ◽

Olivier Coupiac ◽

Nicolas Girard ◽

Gregor Giebel ◽

Tuhfe Göçmen

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Decay Constant ◽

Wind Farms ◽

Power Production ◽

Original Model ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Onshore Wind ◽

Wake Model

Abstract. In this paper, a new calculation procedure to improve the accuracy of the Jensen wake model for operating wind farms is proposed. In this procedure the wake decay constant is updated locally at each wind turbine based on the turbulence intensity measurement provided by the nacelle anemometer. This procedure was tested against experimental data at onshore wind farm La Sole du Moulin Vieux (SMV) in France and the offshore wind farm Horns Rev-I in Denmark. Results indicate that the wake deficit at each wind turbine is described more accurately than when using the original model, reducing the error from 15–20 % to approximately 5 %. Furthermore, this new model properly calibrated for the SMV wind farm is then used for coordinated control purposes. Assuming an axial induction control strategy, and following a model predictive approach, new power settings leading to an increased overall power production of the farm are derived. Power gains found are in the order of 2.5 % for a two wind turbine case with close spacing and 1 to 1.5 % for a row of five wind turbines with a larger spacing. Finally, the uncertainty of the updated Jensen model is quantified considering the model inputs. When checked against the predicted power gain, the uncertainty of the model estimations is seen to be excessive, reaching approximately 4 %, which indicates the difficulty of field observations for such a gain. Nevertheless, the optimized settings are to be implemented during a field test campaign at SMV wind farm in scope of the national project SMARTEOLE.

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