scholarly journals Validation of the dynamic wake meandering model with respect to loads and power production

2021 ◽  
Vol 6 (2) ◽  
pp. 441-460
Author(s):  
Inga Reinwardt ◽  
Levin Schilling ◽  
Dirk Steudel ◽  
Nikolay Dimitrov ◽  
Peter Dalhoff ◽  
...  

Abstract. The outlined analysis validates the dynamic wake meandering (DWM) model based on loads and power production measured at an onshore wind farm with small turbine distances. Special focus is given to the performance of a version of the DWM model that was previously recalibrated at the site. The recalibration is based on measurements from a turbine nacelle-mounted lidar system. The different versions of the DWM model are compared to the commonly used Frandsen wake-added turbulence model. The results of the recalibrated wake model agree very well with the measurements, whereas the Frandsen model overestimates the loads drastically for short turbine distances. Furthermore, lidar measurements of the wind speed deficit as well as the wake meandering are incorporated in the DWM model definition in order to decrease the uncertainties.

2020 ◽  
Author(s):  
Inga Reinwardt ◽  
Levin Schilling ◽  
Dirk Steudel ◽  
Nikolay Dimitrov ◽  
Peter Dalhoff ◽  
...  

Abstract. The outlined analysis validates the dynamic wake meandering (DWM) model based on loads and power production measured at an onshore wind farm with small turbine distances. Special focus is given to the performance of a version of the DWM model that was previously recalibrated at the site. The recalibration is based on measurements from a turbine nacelle-mounted lidar. The different versions of the DWM model are compared to the commonly used Frandsen turbulence model. The results of the recalibrated wake model agree very well with the measurements, whereas the Frandsen model overestimates the loads drastically for short turbine distances. Furthermore, lidar measurements of the wind speed deficit as well as the wake meandering are incorporated in the DWM model definition in order to decrease the uncertainties.


2018 ◽  
Author(s):  
Thomas Duc ◽  
Olivier Coupiac ◽  
Nicolas Girard ◽  
Gregor Giebel ◽  
Tuhfe Göçmen

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.


Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3508 ◽  
Author(s):  
Andrés Guggeri ◽  
Martín Draper

As the size of wind turbines increases and their hub heights become higher, which partially explains the vertiginous increase of wind power worldwide in the last decade, the interaction of wind turbines with the atmospheric boundary layer (ABL) and between each other is becoming more complex. There are different approaches to model and compute the aerodynamic loads, and hence the power production, on wind turbines subject to ABL inflow conditions ranging from the classical Blade Element Momentum (BEM) method to Computational Fluid Dynamic (CFD) approaches. Also, modern multi-MW wind turbines have a torque controller and a collective pitch controller to manage power output, particularly in maximizing power production or when it is required to down-regulate their production. In this work the results of a validated numerical method, based on a Large Eddy Simulation-Actuator Line Model framework, was applied to simulate a real 7.7 MNW onshore wind farm on Uruguay under different wind conditions, and hence operational situations are shown with the aim to assess the capability of this approach to model actual wind farm dynamics. A description of the implementation of these controllers in the CFD solver Caffa3d, presenting the methodology applied to obtain the controller parameters, is included. For validation, the simulation results were compared with 1 Hz data obtained from the Supervisory Control and Data Acquisition System of the wind farm, focusing on the temporal evolution of the following variables: Wind velocity, rotor angular speed, pitch angle, and electric power. In addition to this, simulations applying active power control at the wind turbine level are presented under different de-rate signals, both constant and time-varying, and were subject to different wind speed profiles and wind directions where there was interaction between wind turbines and their wakes.


2017 ◽  
Author(s):  
Steffen Raach ◽  
David Schlipf ◽  
Po Wen Cheng

Abstract. This work presents two advancements towards closed-loop wake redirecting of a wind turbine. First, a model-based wake tracking approach is presented which uses a nacelle-based lidar system facing downwind to obtain information about the wake. The method uses a reduced order wake model to track the wake. The tracking is demonstrated with lidar measurement data from an offshore campaign and with simulated lidar data from a SOWFA simulation. Second, a controller for closed-loop wake steering is presented. It uses the wake tracking information to set the yaw actuator of the wind turbine to redirect the wake to a desired position. Altogether, the two approaches enable a closed-loop wake redirection.


2017 ◽  
Vol 2 (1) ◽  
pp. 257-267 ◽  
Author(s):  
Steffen Raach ◽  
David Schlipf ◽  
Po Wen Cheng

Abstract. This work presents two advancements towards closed-loop wake redirection of a wind turbine. First, a model-based wake-tracking approach is presented, which uses a nacelle-based lidar system facing downwind to obtain information about the wake. The method uses a reduced-order wake model to track the wake. The wake tracking is demonstrated with lidar measurement data from an offshore campaign and with simulated lidar data from a simulation with the Simulator fOr Wind Farm Applications (SOWFA). Second, a controller for closed-loop wake steering is presented. It uses the wake-tracking information to set the yaw actuator of the wind turbine to redirect the wake to a desired position. Altogether, the two approaches enable a closed-loop wake redirection.


2019 ◽  
Vol 4 (2) ◽  
pp. 287-302 ◽  
Author(s):  
Thomas Duc ◽  
Olivier Coupiac ◽  
Nicolas Girard ◽  
Gregor Giebel ◽  
Tuhfe Göçmen

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 the Sole du Moulin Vieux (SMV) onshore wind farm in France and the Horns Rev-I offshore wind farm 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 % to 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 on 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 the scope of the national project SMARTEOLE.


2020 ◽  
Vol 12 (6) ◽  
pp. 2467 ◽  
Author(s):  
Fei Zhao ◽  
Yihan Gao ◽  
Tengyuan Wang ◽  
Jinsha Yuan ◽  
Xiaoxia Gao

To study the wake development characteristics of wind farms in complex terrains, two different types of Light Detection and Ranging (LiDAR) were used to conduct the field measurements in a mountain wind farm in Hebei Province, China. Under two different incoming wake conditions, the influence of wind shear, terrain and incoming wind characteristics on the development trend of wake was analyzed. The results showed that the existence of wind shear effect causes asymmetric distribution of wind speed in the wake region. The relief of the terrain behind the turbine indicated a subsidence of the wake centerline, which had a linear relationship with the topography altitudes. The wake recovery rates were calculated, which comprehensively validated the conclusion that the wake recovery rate is determined by both the incoming wind turbulence intensity in the wake and the magnitude of the wind speed.


2014 ◽  
Vol 494-495 ◽  
pp. 1820-1824
Author(s):  
Dong Ning Wei ◽  
Xue Min Zhang ◽  
Jian Min Ye

In this paper, a novel modelling approach based on characteristic fusion is proposed and used to build a static equivalent model of wind farm. Firstly, the modelling framework based on characteristic fusion is given. Secondly, the basic characteristics of wind farm including characteristic of wind turbine generator (WTG), wind speed spatial distribution and characteristic of wind farm are analyzed according to the framework. Then detailed modelling process is provided utilizing SVR as a fusion tool. This approach combines the advantages of both mechanism and non-mechanism methods with both satisfactory fitting ability and generalization ability. It only requires the maximum and minimum value of wind speed among the wind farm, rather than accurate wake model as mechanism method nor massive measurement data as non-mechanism method. Numerical simulation indicates the effectiveness and robustness of the proposed method. When available data is reduced or includes bad measurement, the proposed method can still keep favorable performance.


2020 ◽  
Author(s):  
Bart M. Doekemeijer ◽  
Stefan Kern ◽  
Sivateja Maturu ◽  
Stoyan Kanev ◽  
Bastian Salbert ◽  
...  

Abstract. The concept of wake steering in wind farms for power maximization has gained significant popularity over the last decade. Recent field trials described in the literature demonstrate the real potential of wake steering on commercial wind farms, but also show that wake steering does not yet consistently lead to an increase in energy production for all inflow conditions. Moreover, a recent survey among experts shows that validation of the concept remains the largest barrier for adoption currently. In response, this article presents the results of a field experiment investigating wake steering in three-turbine arrays at an onshore wind farm in Italy. This experiment was performed as part of the European CL-Windcon project. The measurements show increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions too. In addition to the gains achieved through wake steering at downstream turbines, more interesting to note is that a significant share in gains are from the upstream turbines, showing an increased power production of the yawed turbine itself compared to baseline operation for some wind directions. Furthermore, the surrogate model, while capturing the general trends of wake interaction, lacks the details necessary to accurately represent the measurements. This article supports the notion that further research is necessary, notably on the topics of wind farm modeling and experiment design, before wake steering will lead to consistent energy gains in commercial wind farms.


Author(s):  
Anthony Viselli ◽  
Nathan Faessler ◽  
Matthew Filippelli

This paper presents wind speed measurements collected at 40m to 200m above sea-level to support the New England Aqua Ventus I 12 MW Floating Offshore Wind Farm to be located 17km offshore the Northeast United States. The high-altitude wind speed data are unique and represent some of the first measurements made offshore in this part of the country which is actively being developed for offshore wind. Multiple LiDAR measurements were made using a DeepCLiDAR floating buoy and LiDARs located on land on a nearby island. The LiDARs compared favorably thereby confirming the LiDAR buoy measurements. Wind speed shear profiles are presented. The measurements are compared against industry standard mesoscale model outputs and offshore design codes including the American Bureau of Shipping, American Petroleum Institute, and DNV-GL guides. Significant variation in the vertical wind speed profile occurs throughout the year. This variation is not currently addressed in offshore wind design standards which typically recommend the use of only a few values for wind shear in operational and extreme conditions. The mean wind shears recorded were also higher than industry recommended values. Additionally, turbulence measurements made from the LiDAR, although not widely accepted in the scientific community, are presented and compared against industry guidelines.


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