Periodic dynamic induction control  of wind farms: proving the potential in  simulations and wind tunnel experiments

Frederik, Joeri; Weber, R. J.; Cacciola, Stefano; Campagnolo, Filippo; Croce, Alessandro; Bottasso, Carlo L.; Wingerden, Jan‐Willem van

doi:10.5194/wes-5-245-2020

Cited by 66 publications

(52 citation statements)

References 21 publications

(43 reference statements)

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“…Wind farm control is widely recognized as one of the main solutions to mitigate wake effects (Gebraad et al, 2016;Fleming et al, 2016;Vali et al, 2017;Raach et al, 2018;Fleming et al, 2019). In wind farm control, the turbines in a wind farm operate in a coordinated, collaborative fashion.…”

Section: Introductionmentioning

confidence: 99%

Wind tunnel testing of wake steering with dynamic wind direction changes

et al. 2020

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Abstract. The performance of an open-loop wake-steering controller is investigated with a new unique set of wind tunnel experiments. A cluster of three scaled wind turbines, placed on a large turntable, is exposed to a turbulent inflow and dynamically changing wind directions, resulting in dynamically varying wake interactions. The changes in wind direction were sourced and scaled from a field-measured time history and mirrored onto the movement of the turntable. Exploiting the known, repeatable, and controllable conditions of the wind tunnel, this study investigates the following effects: fidelity of the model used for synthesizing the controller, assumption of steady-state vs. dynamic plant behavior, wind direction uncertainty, the robustness of the formulation in regard to this uncertainty, and a finite yaw rate. The results were analyzed for power production of the cluster, fatigue loads, and yaw actuator duty cycle. The study highlights the importance of using a robust formulation and plant flow models of appropriate fidelity and the existence of possible margins for improvement by the use of dynamic controllers.

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Section: Introductionmentioning

confidence: 99%

Wind tunnel testing of wake steering with dynamic wind direction changes

et al. 2020

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“…Following actuator disk theory (Burton et al, 2011), P p equals 3. However, simulations have shown for the NREL 5 MW turbine that P p equals 1.88 (Gebraad et al, 2016a). Recent work has shown that P p differs for freestream and waked turbines (Liew et al, 2020).…”

Section: Lifting Line Wake Modelmentioning

confidence: 99%

“…with a p, i = 1 2 1 − 1 − C T, i . The parameter η is tuned to match the manufacturer-provided, yaw-aligned C P look-up table (Gebraad et al, 2016a). The applicability of this model is limited to Region II of the wind turbine power curve which is typically between 4 and 15 m s −1 .…”

Section: Lifting Line Wake Modelmentioning

confidence: 99%

“…As such, brute force optimization methods are not sufficient for the selection of the optimal yaw misalignment strategy. Previous studies have considered genetic algorithms (Gebraad et al, 2016a), discrete gradient-based optimization (Gebraad et al, 2017), and analytic gradient-based optimization . Using a gradient-based Adam optimization (Kingma and Ba, 2014), the gradient update is given by…”

Section: Optimal Yaw Misalignment Optimizationmentioning

confidence: 99%

“…Readers are directed to Knudsen et al (2015) and Kheirabadi and Nagamune (2019) for recent reviews of wind farm power maximization methodologies. Previous simulation studies have shown that wake steering may have more potential than static axial induction control for wind farm power maximization (Annoni et al, 2016;Gebraad et al, 2016a;Campagnolo et al, 2016), although dynamic axial induction (Park and Law, 2016;Munters and Meyers, 2018;Frederik et al, 2020) or more sophisticated dynamic blade pitch strategies (Frederik et al, 2019) may significantly increase power production and require future field experimentation.…”

Section: Introductionmentioning

confidence: 99%

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Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions

et al. 2020

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Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions in which the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven ensemble Kalman filter (EnKF) state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six-turbine array embedded in a statistically quasi-stationary, conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller statistically significantly increases power production compared to the baseline, greedy, yaw-aligned control provided that the EnKF estimation is constrained and informed with a physics-based prior belief of the wake model parameters. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that underperform the baseline yaw-aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over the greedy operation, while underestimating the power reduction leads to decreased power; therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. The EnKF-augmented wake model predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02P1, with P1 as the power of the leading turbine at the farm. A standard wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11P1, demonstrating that state estimation improves the predictive capabilities of simplified wake models.

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Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis

et al. 2021

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Wind farm control (WFC) allows coordinated operation of the wind turbines within a wind power plant (WPP). However, its development has traditionally been split into the distinct disciplines of power plant control and farm flow control. As variable renewable energies, in particular wind energy, increase their penetration into the power system, grid code requirements become stricter for WPPs and electricity markets necessarily evolve. In such a context, crossfertilization between the two categories of WFC, targeting an integrated approach where applicable, becomes a priority. An initial overview on how to further align wind farm flow control (WFFC) to grid and electricity markets participation is provided, including an analysis of capabilities and prospects. In addition, greater detail is given about an open‐access dataset serving as market showcases for value demonstration of price‐driven operation of WPPs by means of WFFC.

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Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments

Cited by 66 publications

References 21 publications

Wind tunnel testing of wake steering with dynamic wind direction changes

Wind tunnel testing of wake steering with dynamic wind direction changes

Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions

Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis

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