Wind farm power maximization based on a cooperative static game approach

Park, Jinkyoo; Kwon, Soon-Duck; Law, Kincho H.

doi:10.1117/12.2009618

Cited by 57 publications

(47 citation statements)

References 16 publications

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“…Recently, there has been a push towards the optimization in the control of power generated by an entire large wind farm, as opposed to operating each turbine in a maximum power point tracking manner [10,11]. In this vane, the wake deflection by operating wind turbines in yaw has been shown to be an attractive option to control wake deflection and power output [10,[12][13][14][15][16], and has generated significant interest recently [9,17,18].…”

Section: Introductionmentioning

confidence: 99%

Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions

Howland

Bossuyt

Martínez‐Tossas

et al. 2016

Journal of Renewable and Sustainable Energy

237

210

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Reducing wake losses in wind farms by deflecting the wakes through turbine yawing has been shown to be a feasible wind farm controls approach. Nonetheless, the effectiveness of yawing depends not only on the degree of wake deflection but also on the resulting shape of the wake. In this work, the deflection and morphology of wakes behind a wind turbine operating in yawed conditions are studied using wind tunnel experiments of a wind turbine modeled as a porous disk in a uniform inflow. First, by measuring velocity distributions at various downstream positions and comparing with prior studies, we confirm that the non-rotating wind turbine model in yaw generates realistic wake deflections. Second, we characterize the wake shape and make first observations of what is termed a curled wake, displaying significant spanwise asymmetry. The wake curling observed in the experiments is also reproduced qualitatively in large eddy simulations using both actuator disk and actuator line models. When a wind turbine is yawed for the benefit of downstream turbines, the asymmetric shape of the wake must be taken into account since it affects how much of it intersects the downstream turbines.PREPRINT, submitted to the Journal of Renewable and Sustainable Energy (January 2016) Ref. [14] used two aligned turbines in a wind tunnel and tested varying the rotor yaw angle, tip speed ratio, and the blade pitch of the upstream wind turbine only. This study showed that varying the yaw angle of the wind turbine was of comparable benefit to increasing the streamwise spacing between turbines, with an optimal power output occurring at 30 • . Refs. [17,18] studied the effects of controlling yaw angle, tip speed ratio, and the blade pitch of the upstream turbine for scaled model wind turbines, with results also revealing the benefits of yawing the upstream turbine. Further, yaw misalignment has been shown to reduce the steady-state blade loading variations by up to 70%, which has lead to the use of yawing to increase operational life [19]. Ref.[20] studied a rotating wind turbine model in replicated atmospheric boundary layer conditions to discover a deflection of approximately 0.6D in the far wake.Refs. [9,[21][22][23] were computational studies of wake deflection using various yaw angles. Ref.[21] uses LES with an actuator disk model with turbulent inflow and shows that wake deflection can be reproduced in such simulations. They also propose a momentum-based model for the deflection which is compared to LES with reasonable validity in the far wake. Some experimental results are compared, but the authors cite a need for more experimental verification before a wake controller may be developed.Ref.[9] studied wake deflection under various conditions using the SOWFA Large Eddy Simulation (LES) code and using the NREL 5 MW turbine model [24]. When the yaw angle γ was γ = 30 • , the study found the maximum wake deflection to reach about 0.5D in the far wake, where D is the rotor diameter. Ref.[22] studied the near wake structure of a wind turbine unde...

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Section: Introductionmentioning

confidence: 99%

Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions

Howland

Bossuyt

Martínez‐Tossas

et al. 2016

Journal of Renewable and Sustainable Energy

237

210

View full text Add to dashboard Cite

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“…1 Introduction 10 Flow control in wind power plants aims to manipulate a turbine's influence on the wind to achieve an increase in performance at the plant level. One method of flow control known as wake steering intentionally misaligns turbines from the incoming flow to deflect their wakes away from downstream turbines (Wagenaar et al, 2012;Adaramola and Krogstad, 2011;Park et al, 2013;Gebraad et al, 2016). Research has shown that with certain misalignments the overall production of a wind plant can increase, even though the misaligned turbines experience an individual power loss (Dahlberg and Medici, 2003).…”

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confidence: 99%

Unlocking the Full Potential of Wake Steering: Implementation and Assessment of a Controls-Oriented Model

Bay

King

Fleming

et al. 2019

Preprint

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In this work, a controls-oriented wake model is modified and compared to an analytical Gaussian wake model and high-fidelity simulation data. This model, called the curled wake model, captures a wake phenomenon that occurs behind yawed turbines, modeled as a collection of vortices shed from the rotor plane. Through turbine simulations, these vortices are shown to have a significant impact on the prediction of wake steering's performance. Also, optimizations using the model are performed and produce results consistent with recent published research. Results indicate that wind farm controllers designed 5 and analyzed with the curled wake model produce wake steering controllers which can realize larger gains in power production than previously estimated. Overall, the results support the concept of secondary steering, or a yawed turbine's ability to deflect the wake of a downstream turbine, and suggest that future turbine wake studies and yaw optimizations should include the curled wake phenomenon.have been conducted to evaluate the effectiveness of wake steering (Wagenaar et al., 2012;Fleming et al., 2017). Fleming et al. (2019) found a 13% increase in energy on a downstream turbine for two closely spaced turbines within a 10 • bin, consistent with prior predictions from different models. Howland et al. (2019) leveraged site-specific, historical operational data to develop a wake control scheme that was tested in a land-based wind power plant in Alberta, Canada. The controller resulted in power increases of 7%-47% for wind conditions that commonly occur at night, as well as a decrease in variability 5 of power production of up to 72%.Alongside theoretical and experimental studies, engineering models have been crafted to predict wake steering and assess its overall benefit. One controls-oriented tool used in the design and study of turbine wake controls is the FLOw Redirection and Induction in Steady State (FLORIS) model . Originally based on the Jensen (Jensen, 1984) andJiménez (Jiménez et al., 2010) models, FLORIS has evolved into a software repository that contains several wake models. More 10

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“…There, distributed learning algorithms have been applied with both local and global knowledge, which are available to each turbine in a wind farm configuration. Another type of GT based approach has been proposed in [13] by considering two control variables of wind turbines. However, these approaches are validated only for a static model of the wind farm, and rely on off-line computation.…”

Section: Introductionmentioning

confidence: 99%

A Model-Free Approach for Maximizing Power Production of Wind Farm Using Multi-Resolution Simultaneous Perturbation Stochastic Approximation

2014

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This paper provides a model-free approach based on the Multi-Resolution Simultaneous Perturbation Stochastic Approximation (MR-SPSA) for maximizing power production of wind farms. The main advantage is that the method based on MR-SPSA can achieve fast controller tuning without any plant model by exploiting the information of the wind farm configuration such as turbines location and wind direction. In order to simulate the performance of the model-free scheme, a wind farm model with dynamic characterization of wake interaction between turbines is used and then the proposed method is applied to the Horns Rev wind farm. Simulation results illustrate that the method based on MR-SPSA achieves the maximum total power production with faster convergence compared with other existing model-free methods.

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Wind farm power maximization based on a cooperative static game approach

Cited by 57 publications

References 16 publications

Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions

Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions

Unlocking the Full Potential of Wake Steering: Implementation and Assessment of a Controls-Oriented Model

A Model-Free Approach for Maximizing Power Production of Wind Farm Using Multi-Resolution Simultaneous Perturbation Stochastic Approximation

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