Three-Dimensional LiDAR Wake Measurements in an Offshore Wind Farm and Comparison with Gaussian and AL Wake Models

Huaneng Rudong 300 MW offshore wind farm project is located in eastern China. The wake effect is one of the major concerns for wind farm operators, as all 70 units are plotted in ranks, and the sea surface roughness is low. This paper investigated the wake intensity by combining a field test and a numerical simulation. To carry out further yaw optimization, a Gaussian wake model was adopted. Firstly, a 3D Light Detection and Ranging device (LiDAR) was used to capture the features in both horizontal and vertical directions of the wake. It indicated that Gaussian wake model can precisely predict the characteristics under time average and steady state in the wind farm. The predicted annual energy production (AEP) of the whole wind farm by the Gaussian model is compared with the calculation result of the actuator line (AL)-based LES method, and the difference between the two methods is mostly under 10%.

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Adequacy evaluation of offshore wind farm considering wake effect

2017 China International Electrical and Energy Conference (CIEEC) ◽

10.1109/cieec.2017.8388481 ◽

2017 ◽

Author(s):

Ziehen Zheng ◽

Yang Fu ◽

Shuai Shi

Keyword(s):

Wind Farm ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Wake Effect

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Estimation of Offshore Wind Farm Reliability Considering Wake Effect and Wind Turbine Failure

2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) ◽

10.1109/isgt-asia.2019.8880887 ◽

2019 ◽

Author(s):

Angshu Plavan Nath ◽

Santanu Paul ◽

Zakir Hussain Rather ◽

Sadhan Mahapatra

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Wake Effect

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Theory and Verification of a new 3D RANS Wake Model

10.5194/wes-2020-33 ◽

2020 ◽

Author(s):

Philip Bradstock ◽

Wolfgang Schlez

Keyword(s):

Wind Farm ◽

Three Dimensional ◽

Wind Farms ◽

Offshore Wind ◽

Offshore Wind Farms ◽

Ambient Flow ◽

Turbulence Decay ◽

Local Shear ◽

Time Lagged ◽

Wake Model

Abstract. This paper details the background to the WakeBlaster model: a purpose built, parabolic three-dimensional RANS solver, developed by ProPlanEn. WakeBlaster is a field model, rather than a single turbine model; it therefore eliminates the need for an empirical wake superposition model. It belongs to a class of very fast (a few core seconds, per flow case) mid-fidelity models, which are designed for industrial application in wind farm design, operation and control. The domain is a three-dimensional structured grid, with approximately 80 nodes covering the rotor disk, by default. WakeBlaster uses eddy viscosity turbulence closure, which is parameterized by the local shear, time-lagged turbulence development, and stability corrections for ambient shear and turbulence decay. The model prescribes a profile at the end of the near-wake, and the spatial variation of ambient flow, by using output from an external flow model. The WakeBlaster model is verified, calibrated and validated using a large volume of data from multiple onshore and offshore wind farms. This paper presents example simulations for one offshore wind farm.

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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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Dynamic Seabed Response Around Wind Turbine Foundation: Donghai Offshore Wind Farm China

Volume 6: Polar and Arctic Sciences and Technology; Offshore Geotechnics; Petroleum Technology Symposium ◽

10.1115/omae2013-10221 ◽

2013 ◽

Author(s):

K. T. Chang ◽

D.-S. Jeng

Keyword(s):

Pore Pressure ◽

Wind Farm ◽

Stokes Equations ◽

Three Dimensional ◽

Energy System ◽

Offshore Wind ◽

Degree Of Saturation ◽

Navier Stokes ◽

Offshore Wind Farm ◽

Wave Induced

Donghai offshore wind farm, the first and largest commercial operating offshore wind energy system in China, adopted a novel foundation–high-rising structure foundation. In this paper, a three-dimensional porous model, based on Reynolds-Averaged Navier-Stokes equations and Biot’s poro-elastic theory, was developed by integrating 3D wave and seabed models to simulate wave-induced seabed response around the high-rising structure foundation. Then, a parametric study for the wave and seabed characteristics on the foundation stability was conducted. The numerical results concluded from the numerical analysis were as follows: (i) the existence of structure had a significant effect on the wave transformations and the distributions of wave-induced pore pressures; (ii) the magnitude of wave-induced pore pressure increased as wave height or wave period increased; (iii) the dissipation rate of pore pressure increased as the degree of saturation decreased.

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