A Steady-State Wind Farm Wake Model Implemented in OpenFAST

Wake models play a fundamental role in finding optimized solutions in wind farm control. In fact, they allow assessing how wakes develop and interact with each other with the agility required for real-time applications. In this paper, a Gaussian Wake Model (GWM) is implemented in the OpenFAST framework in a way such that its fidelity is increased with respect to previously implemented models, while enhancing its compatibility with control purposes. The OpenFAST tool is coupled with Floris, NREL’s software based on the GWM, in order to simulate the wake effect on downstream machines (in the case where the downstream rotor is fully covered by the wake, only partially covered by the wake, of the wake is generated by the interaction of more than one turbine), while the rotor aerodynamics is calculated using the BEMT on the actual rotor flow field. We intend this work as a starting point for developing and testing open/closed-loop control logics that will work in real wind farms. To show the suitability of the implementation, the entire model is then compared to Floris.

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A control-oriented dynamic wind farm model: WFSim

Wind Energy Science ◽

10.5194/wes-3-75-2018 ◽

2018 ◽

Vol 3 (1) ◽

pp. 75-95 ◽

Cited By ~ 22

Author(s):

Sjoerd Boersma ◽

Bart Doekemeijer ◽

Mehdi Vali ◽

Johan Meyers ◽

Jan-Willem van Wingerden

Keyword(s):

Wind Farm ◽

Closed Loop ◽

Stokes Equations ◽

Three Dimensional ◽

Wind Farms ◽

Vertical Dimension ◽

High Fidelity ◽

Loop Control ◽

Starting Point ◽

Farm Model

Abstract. Wind turbines are often sited together in wind farms as it is economically advantageous. Controlling the flow within wind farms to reduce the fatigue loads, maximize energy production and provide ancillary services is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, we present a control-oriented dynamical wind farm model called the WindFarmSimulator (WFSim) that can be used in closed-loop wind farm control algorithms. The three-dimensional Navier–Stokes equations were the starting point for deriving the control-oriented dynamic wind farm model. Then, in order to reduce computational complexity, terms involving the vertical dimension were either neglected or estimated in order to partially compensate for neglecting the vertical dimension. Sparsity of and structure in the system matrices make this model relatively computationally inexpensive. We showed that by taking the vertical dimension partially into account, the estimation of flow data generated with a high-fidelity wind farm model is improved relative to when the vertical dimension is completely neglected in WFSim. Moreover, we showed that, for the study cases considered in this work, WFSim is potentially fast enough to be used in an online closed-loop control framework including model parameter updates. Finally we showed that the proposed wind farm model is able to estimate flow and power signals generated by two different 3-D high-fidelity wind farm models.

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Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation

Energies ◽

10.3390/en14010052 ◽

2020 ◽

Vol 14 (1) ◽

pp. 52

Author(s):

Michael F. Howland ◽

John O. Dabiri

Keyword(s):

Wind Farm ◽

Closed Loop ◽

Power Production ◽

Model Parameters ◽

Closed Loop Control ◽

Steering Control ◽

Loop Control ◽

Large Eddy ◽

Wake Model ◽

The Impact

Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically adjust to evolving wind conditions, wherein the optimal wake model parameters are estimated as a function of time in a hybrid physics- and data-driven approach using supervisory control and data acquisition (SCADA) data. Analytic wake models rely on wake velocity deficit superposition methods to generalize the individual wake deficit to collective wind farm flow. In this study, the impact of the wake model superposition methodologies on closed-loop control are tested in large eddy simulations of the conventionally neutral atmospheric boundary layer with full Coriolis effects. A model for the non-vanishing lateral velocity trailing a yaw misaligned turbine, termed secondary steering, is also presented, validated, and tested in the closed-loop control framework. Modified linear and momentum conserving wake superposition methodologies increase the power production in closed-loop wake steering control statistically significantly more than linear superposition. While the secondary steering model increases the power production and reduces the predictive error associated with the wake model, the impact is not statistically significant. Modified linear and momentum conserving superposition using the proposed secondary steering model increase a six turbine array power production, compared to baseline control, in large eddy simulations by 7.5% and 7.7%, respectively, with wake model predictive mean absolute errors of 0.03P1 and 0.04P1, respectively, where P1 is the baseline power production of the leading turbine in the array. Ensemble Kalman filter parameter estimation significantly reduces the wake model predictive error for all wake deficit superposition and secondary steering cases compared to predefined model parameters.

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A control-oriented dynamic wind farm model: WFSim

10.5194/wes-2017-44 ◽

2017 ◽

Author(s):

Sjoerd Boersma ◽

Bart Doekemeijer ◽

Mehdi Vali ◽

Johan Meyers ◽

Jan-Willem van Wingerden

Keyword(s):

Wind Farm ◽

Closed Loop ◽

Stokes Equations ◽

Three Dimensional ◽

Wind Farms ◽

Vertical Dimension ◽

Time Varying ◽

Atmospheric Conditions ◽

Loop Control ◽

Farm Model

Abstract. Wind turbines are often sited together in wind farms as it is economically advantageous. Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities such as the delivery of a demanded power is a challenging control problem due to the underlying time-varying nonlinear wake dynamics. It is therefore important to use the closed-loop control paradigm since it can partially account for model uncertainty and, in addition, it can deal with unknown disturbances. State-of-the-art closed-loop dynamic wind farm controllers are based on computationally expensive wind farm models, which make these methods suitable for analysis though unsuitable for online control. The latter is important, because it allows for model adaptation to the time-varying atmospheric conditions using SCADA measurements. As a consequence, more reliable control settings can be evaluated. In this paper, a dynamic wind farm model suitable for online wind farm control will be presented. The derivation of the control-oriented dynamic wind farm model starts with the three-dimensional Navier-Stokes equations. Then, terms involving the vertical dimension will be estimated in order to partially compensate for neglecting the vertical dimension or neglected such that a 2D-like dynamic wind farm model will be obtained. Sparsity of and structure in the system matrices make this model relatively computational inexpensive hence suitable for online closed-loop controller synthesis including model parameter updates. Flow and power data evaluated with the wind farm model presented in this work will be validated with high fidelity flow data.

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The Impact of Visual Feedback Type on the Mastery of Visuo-Motor Transformations

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000084 ◽

2012 ◽

Vol 220 (1) ◽

pp. 3-9 ◽

Cited By ~ 4

Author(s):

Sandra Sülzenbrück

Keyword(s):

Empirical Research ◽

Visual Feedback ◽

Closed Loop ◽

Motor Planning ◽

Visual Input ◽

Closed Loop Control ◽

Loop Control ◽

Feedback Type ◽

Effective Use ◽

The Impact

For the effective use of modern tools, the inherent visuo-motor transformation needs to be mastered. The successful adjustment to and learning of these transformations crucially depends on practice conditions, particularly on the type of visual feedback during practice. Here, a review about empirical research exploring the influence of continuous and terminal visual feedback during practice on the mastery of visuo-motor transformations is provided. Two studies investigating the impact of the type of visual feedback on either direction-dependent visuo-motor gains or the complex visuo-motor transformation of a virtual two-sided lever are presented in more detail. The findings of these studies indicate that the continuous availability of visual feedback supports performance when closed-loop control is possible, but impairs performance when visual input is no longer available. Different approaches to explain these performance differences due to the type of visual feedback during practice are considered. For example, these differences could reflect a process of re-optimization of motor planning in a novel environment or represent effects of the specificity of practice. Furthermore, differences in the allocation of attention during movements with terminal and continuous visual feedback could account for the observed differences.

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