Network based estimation of wind farm power and velocity data under changing wind direction

Starke, Genevieve M.; Stanfel, Paul; Meneveau, Charles; Gayme, Dennice F.; King, Jennifer

doi:10.23919/acc50511.2021.9483060

Cited by 12 publications

(12 citation statements)

References 25 publications

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“…2) Yaw Controller: The yaw controller uses a modelconstrained optimal control framework to determine the yaw commands for each turbine over a finite look-ahead time period. The outer loop yaw control uses a model-constrained optimal control based on (1) constrained by the dynamic yaw model introduced in [25]. This model represents the wind farm as a graph, where the turbines are the nodes of the graph and the interactions between the turbines are the edges of the graph.…”

Section: B Controller Frameworkmentioning

confidence: 99%

“…This work seeks to build upon the previously demonstrated yaw studies in the following ways. In order to implement a combined yaw-pitch control strategy for power tracking, we use an optimization based control of the yaw angles that employs a recently developed dynamic yaw model that enables us to capture the transient response of the yaw actions that were not taken into account in previous work based on static yaw configurations [25]. The control also takes into account the physical rate at which a turbine can yaw by including a rate limit for the yaw actuation.…”

Section: Introductionmentioning

confidence: 99%

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Yaw-Augmented Control for Wind Farm Power Tracking

Starke

Meneveau

King

et al. 2023

2023 American Control Conference (ACC)

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Section: B Controller Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Yaw-Augmented Control for Wind Farm Power Tracking

Starke

Meneveau

King

et al. 2023

2023 American Control Conference (ACC)

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“…The communication protocol between upstream and downstream neighbours is constructed based on a directed graph network, similar to, for example, the work by Starke et al (2021). The structure of this network naturally changes with the wind direction as wakes propagate with the flow through the farm.…”

Section: Directed Graph Networkmentioning

confidence: 99%

Dynamic wind farm flow control using free-vortex wake models

van den Broek,

Becker,

Sanderse

et al. 2023

Preprint

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Abstract. A novel dynamic economic model-predictive control strategy is presented that improves wind farm power production and reduces the additional demands of wake steering on yaw actuation when compared to an industry state-of-the-art reference controller. The novel controller takes a distributed approach to yaw control optimisation using a free-vortex wake model. An actuator-disc representation of the wind turbine is employed and adapted to the wind-farm scale by modelling secondary effects of wake steering and connecting individual turbines through a directed graph network. The economic model-predictive control problem is solved on a receding horizon using gradient-based optimisation, demonstrating sufficient performance for realising real-time control. The novel controller is tested in a large-eddy simulation environment and compared against a state-of-the-art look-up table approach based on steady-state model optimisation. Under realistic variations in wind direction and wind speed, the novel controller yields additional gains in power production during transients as well as a reduction in yaw actuator usage.

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“…Efforts have also been made to train reduced-order models of low-fidelity using data from numerical simulations. In conjunction with graph-based methods, such data-driven models have been used to estimate the direction of free-stream velocity 44 , identify clusters of wind turbines within farms 45 , or even predict variations in power output due to changes in inlet wind direction 46 . In a similar vein, machine learning approaches have been used to obtain reduced-order models based on data collected from experiments and numerical simulations 47,48,49,50 .…”

Section: Control-oriented Wake Modelingmentioning

confidence: 99%

Toward stochastic dynamical wake-modeling for wind farms

Bhatt

Zare

2022

2022 American Control Conference (ACC)

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Low-fidelity analytical models of turbine wakes have traditionally been used for wind farm planning, performance evaluation, and demonstrating the utility of advanced control algorithms in increasing the annual energy production. In practice, however, it remains challenging to correctly estimate the flow and achieve significant performance gains using conventional low-fidelity models. This is due to the over-simplified static nature of wake predictions from models that are agnostic to the effects of atmospheric boundary layer turbulence and the complex aerodynamic interactions among wind turbines. To improve the predictive capability of low-fidelity models while remaining amenable to control design, we offer a stochastic dynamical modeling framework for capturing the effect of atmospheric turbulence on the thrust force and power generation as determined by the actuator disk concept. In this approach, we use stochastically forced linear models of the turbulent velocity field to augment the analytically computed wake velocity and achieve consistency with higher-fidelity models in capturing power and thrust force measurements. The powerspectral densities of our stochastic models are identified via convex optimization to ensure consistency with partially available velocity statistics or power and thrust force measurements while preserving model parsimony. We demonstrate the utility of our approach in capturing turbulence intensity variations behind wind turbines and estimating thrust force and power signals generated by large-eddy simulations of the flow over a cascade of turbines.Our results provide insight into the significance of sparse field measurements in recovering the statistical signature of wind farm turbulence using stochastic linear models.

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Network based estimation of wind farm power and velocity data under changing wind direction

Cited by 12 publications

References 25 publications

Yaw-Augmented Control for Wind Farm Power Tracking

Yaw-Augmented Control for Wind Farm Power Tracking

Dynamic wind farm flow control using free-vortex wake models

Toward stochastic dynamical wake-modeling for wind farms

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