scholarly journals Feedforward-Feedback wake redirection for wind farm control

2019 ◽  
Author(s):  
Steffen Raach ◽  
Bart Doekemeijer ◽  
Sjoerd Boersma ◽  
Jan-Willem van Wingerden ◽  
Po Wen Cheng
Keyword(s):  
Wind Farm ◽  
Atmospheric Condition ◽  
Total Power ◽  
Test Case ◽  
Feedback Controllers ◽  
Baseline Case ◽  
Lidar Measurements ◽  
Local Feedback ◽  
Feedforward Controller ◽  
Wake Model

Abstract. This work presents a combined feedforward-feedback wake redirection framework for wind farm control. The FLORIS wake model, a control-oriented steady-state wake model is used to calculate optimal yaw angles for a given wind farm layout and atmospheric condition. The optimal yaw angles, which maximize the total power output, are applied to the wind farm. Further, the lidar-based closed-loop wake redirection concept is used to realize a local feedback on turbine level. The wake center is estimated from lidar measurements 3D downwind of the wind turbines. The dynamical feedback controllers support the feedforward controller and reject disturbances and adapt to model uncertainties. Altogether, the total framework is presented and applied to a nine turbine wind farm test case. In a high fidelity simulation study the concept shows promising results and an increase in total energy production compared to the baseline case and the feedforward-only case.

scholarly journals Optimizing Wind Farm Control through Wake Steering using Surrogate Models based on High Fidelity Simulations

2019 ◽  
Author(s):  
Paul Hulsman ◽  
Søren Juhl Andersen ◽  
Tuhfe Göçmen
Keyword(s):  
Wind Farm ◽  
Control Strategies ◽  
Atmospheric Condition ◽  
Surrogate Models ◽  
Normal Operation ◽  
Total Power ◽  
High Fidelity ◽  
Power Gain ◽  
Model Errors ◽  
Aeroelastic Simulations

Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. Both the power output and the loading of the turbines are included in the optimization of wind farm control strategies. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°. At larger spacing, the power gain the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction of the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, and the implication for wind farm control.

scholarly journals A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3537
Author(s):  
Jian Teng ◽  
Corey D. Markfort
Keyword(s):  
Wind Turbine ◽  
Analytical Approach ◽  
Wind Farm ◽  
Renewable Energy Sources ◽  
Calibration Procedure ◽  
Total Power ◽  
Wake Effects ◽  
Wake Model ◽  
Operational Data ◽  
Wake Modeling

Wind energy is one of the fastest growing renewable energy sources in the U.S. Wind turbine wakes change the flow field within wind farms and reduce power generation. Prior research has used experimental and computational methods to investigate and model wind farm wake effects. However, these methods are costly and time-consuming to use commercially. In contrast, a simple analytical approach can provide reasonably accurate estimates of wake effects on flow and power. To reducing errors in wake modeling, one must calibrate the model based on a specific wind farm setting. The purpose of this research is to develop a calibration procedure for wind farm wake modeling using a simple analytical approach and wind turbine operational data obtained from the Supervisory Control And Data Acquisition (SCADA) system. The proposed procedure uses a Gaussian-based analytical wake model and wake superposition model. The wake growth rate varies across the wind farm based on the local streamwise turbulence intensity. The wake model was calibrated by implementing the proposed procedure with turbine pairs within the wind farm. The performance of the model was validated at an onshore wind farm in Iowa, USA. The results were compared with the industry standard wind farm wake model and shown to result in an approximate 1% improvement in sitewide total power prediction. This new SCADA-based calibration procedure is useful for real-time wind farm operational optimization.

Optimizing the Unrestricted Placement of Turbines of Differing Rotor Diameters in a Wind Farm for Maximum Power Generation

2010 ◽  
Author(s):  
Souma Chowdhury ◽  
Achille Messac ◽  
Jie Zhang ◽  
Luciano Castillo ◽  
Jose Lebron
Keyword(s):  
Power Generation ◽  
Wind Farm ◽  
Pso Algorithm ◽  
Total Power ◽  
Wake Effect ◽  
Swarm Optimization ◽  
Wind Farm Layout Optimization ◽  
Farm Efficiency ◽  
Wake Model ◽  
Velocity Deficits

This paper presents a new method (the Unrestricted Wind Farm Layout Optimization (UWFLO)) of arranging turbines in a wind farm to achieve maximum farm efficiency. The powers generated by individual turbines in a wind farm are dependent on each other, due to velocity deficits created by the wake effect. A standard analytical wake model has been used to account for the mutual influences of the turbines in a wind farm. A variable induction factor, dependent on the approaching wind velocity, estimates the velocity deficit across each turbine. Optimization is performed using a constrained Particle Swarm Optimization (PSO) algorithm. The model is validated against experimental data from a wind tunnel experiment on a scaled down wind farm. Reasonable agreement between the model and experimental results is obtained. A preliminary wind farm cost analysis is also performed to explore the effect of using turbines with different rotor diameters on the total power generation. The use of differing rotor diameters is observed to play an important role in improving the overall efficiency of a wind farm.

An Extended Pattern Search Approach to Wind Farm Layout Optimization

2010 ◽  
Author(s):  
Bryony L. Du Pont ◽  
Jonathan Cagan
Keyword(s):  
Wind Farm ◽  
Search Algorithm ◽  
Turbine Blades ◽  
Pattern Search ◽  
Total Power ◽  
Local Optima ◽  
Wake Model ◽  
Power Capability ◽  
Search Approach ◽  
Extended Pattern Search

An extended pattern search approach is presented for optimizing the placement of wind turbines on a wind farm. The algorithm will develop a two-dimensional layout for a given number of turbines, employing an objective function that minimizes costs while maximizing the total power production of the farm. The farm cost is developed using an established simplified model that is a function of the number of turbines. The power development of the farm is estimated using an established simplified wake model, which accounts for the aerodynamic effects of turbine blades on downstream wind speed, to which the power output is directly proportional. The interaction of the turbulent wakes developed by turbines in close proximity largely determines the power capability of the farm. As pattern search algorithms are deterministic, multiple extensions are presented to aid escaping local optima by infusing stochastic characteristics into the algorithm. This stochasticity improves the algorithm’s performance, yielding better results than purely deterministic search methods. Three test cases are presented: a) constant, unidirectional wind, b) constant, multidirectional wind, and c) varying, multidirectional wind. Resulting layouts developed by this extended pattern search algorithm develop more power than previously explored algorithms with the same evaluation models and objective functions. In addition, the algorithm’s layouts motivate a heuristic that yields the best layouts found to date.

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 ◽  
...  
Keyword(s):  
Wind Speed ◽  
Turbulence Model ◽  
Wind Farm ◽  
Power Production ◽  
Special Focus ◽  
Lidar System ◽  
Onshore Wind ◽  
Model Based ◽  
Lidar Measurements ◽  
Wake Model

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.

scholarly journals Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

2020 ◽  
Vol 5 (1) ◽  
pp. 309-329 ◽  
Author(s):  
Paul Hulsman ◽  
Søren Juhl Andersen ◽  
Tuhfe Göçmen
Keyword(s):  
Wind Farm ◽  
Atmospheric Condition ◽  
Surrogate Models ◽  
Normal Operation ◽  
Total Power ◽  
High Fidelity ◽  
Model Errors ◽  
Turbine Performance ◽  
Aeroelastic Simulations ◽  
The Cost

Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high-fidelity flow simulations coupled with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. The wind farm control strategy is optimized for both the power output and the loading of the turbines. The optimization performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggests that a power gain of almost 3%±1% can be achieved at close spacing by yawing the upstream turbine more than 15∘. At larger spacing the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction in the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, as well as the implications for wind farm control.

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

2020 ◽  
Author(s):  
Inga Reinwardt ◽  
Levin Schilling ◽  
Dirk Steudel ◽  
Nikolay Dimitrov ◽  
Peter Dalhoff ◽  
...  
Keyword(s):  
Wind Speed ◽  
Turbulence Model ◽  
Wind Farm ◽  
Power Production ◽  
Special Focus ◽  
Onshore Wind ◽  
Model Based ◽  
Lidar Measurements ◽  
Wake Model

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.

scholarly journals Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements

2020 ◽  
Vol 12 (6) ◽  
pp. 2467 ◽  
Author(s):  
Fei Zhao ◽  
Yihan Gao ◽  
Tengyuan Wang ◽  
Jinsha Yuan ◽  
Xiaoxia Gao
Keyword(s):  
Wind Speed ◽  
Wind Shear ◽  
Wind Farm ◽  
Wind Farms ◽  
Field Measurements ◽  
Development Trend ◽  
Asymmetric Distribution ◽  
Lidar Measurements ◽  
Wind Characteristics ◽  
The Development Trend

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.

Reliability Modeling of Wind Farm Considering the Outages of Connection Cables

2013 ◽  
Vol 291-294 ◽  
pp. 461-466
Author(s):  
Guo Bing Qiu ◽  
Wen Xia Liu ◽  
Jian Hua Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power Output ◽  
Complex Terrain ◽  
Wind Farm ◽  
Sequential Monte Carlo ◽  
Reliability Model ◽  
Wake Effect ◽  
Reliability Modeling ◽  
Wake Model

Considering the randomness of wind speed and wind direction, the partial wake effect between wind turbines (WTs) in complex terrain was analyzed and a multiple wake model in complex terrain was established. Taking the power output characteristic of WT into consideration, a wind farm reliability model which considered the outages of connection cables was presented. The model is implemented in MATLAB using sequential Monte Carlo simulation and the results show that this model corrects the power output of wind farm, while improving the accuracy of wind farm reliability model.

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