Dynamic Flow Modelling for Model-Predictive Wind Farm Control

Broek, Maarten J. van den; Wingerden, Jan‐Willem van

doi:10.1088/1742-6596/1618/2/022023

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…Although it is the key priority for advancing wind farm control technology (van Wingerden et al, 2020), the field test and validation for WFFC-oriented models are significantly challenging due to the stochasticity, non-stationarity, high variability and overall uncertainty. Specifically for the FarmConners benchmark, the highlights of the participating model performance for the wind farm field data blind test can be summarised as below.…”

Section: Summary Of the Wind Farm Field Data Blind Testmentioning

confidence: 99%

FarmConners Wind Farm Flow Control Benchmark: Blind Test Results

Göçmen

Campagnolo²,

Duc

et al. 2022

Preprint

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Abstract. Wind farm flow control (WFFC) is a topic of interest at several research institutes, industry and certification agencies world-wide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments and high-fidelity simulations. Within that database, 4 blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wake under WFFC. Some participants took part in several blind tests and some participants have implemented several models. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. Majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration is particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.

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Section: Summary Of the Wind Farm Field Data Blind Testmentioning

confidence: 99%

FarmConners Wind Farm Flow Control Benchmark: Blind Test Results

Göçmen

Campagnolo²,

Duc

et al. 2022

Preprint

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show abstract

“…This hysteresis effect is a consequence of a wake curvature that happens for sudden changes in wind direction and results in a greater impact on downwind turbines when they are no longer collinear with wind direction. In addition, in [5] and [6] the curvature was analyzed by simulating a wind direction change in a wind farm arranged in a regular 3 × 3 grid. Also, in [7] a real situation of dynamic changes in wind speed and direction was analyzed, showing similar results regarding the bending of the wake.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding yaw correction in every time step, it turns out computationally expensive because a subroutine must is executed to find the new position for each node of the ADM every time. One possible alternative to overcame this difficulty is to enforce constant ADM direction, as reported in [6]. Other possibility would be to enforce the same direction for ADM as the wind so that the position for all nodes is known from the beginning [5,1].…”

Section: Introductionmentioning

confidence: 99%

Transient phenomena study in wind energy by means of LES simulations: impact of wind direction changes

Barile,

Navarro Diaz,

Sosa

et al. 2023

J. Phys.: Conf. Ser.

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Transient phenomena such as abrupt changes in wind direction occur frequently during the operation of wind farms. The wakes produced behind wind turbines are affected by these situations, modifying the impact on nearby turbines, both in power output and life expectancy of the blades. In this work, a group of 4 turbines in a wind farm is analysed via Computational Fluid Dynamic (CFD) simulations during wind shifts. Large Eddy Simulations (LES) are carried out by means of the open-source software OpenFOAM, including the SOWFA libraries. Once the wakes reach a time average stationary solution the wind direction is modified by means of a pressure gradient forcing, imposing different change rates. An actuator disk with the ability to correct yaw misalignment is used to represent turbines. Besides, neutral Atmospheric Boundary Layer (ABL) flows are considered. Results show that for higher change rates the greatest impact on the downwind turbine is produced when turbines are no longer collinear with wind direction due to the wake curvature. Also, higher change rates produce a higher curvature modifying the impact on power output of downwind turbines. This study provides a deeper understanding of the cause of greater deficits in wind farm output.

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“…The basis for this new control strategy is a wind farm flow modelling tool suitable for adjoint optimisation of controls that simulates two-dimensional (2D) flow for computational efficiency [5,6] which was inspired by control optimisation using a high-fidelity adjoint LES simulation [7]. It has been incorporated in FRED -Framework for wind farm flow Regulation and Estimation with Dynamics [8].…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of this work is twofold. First, we extend the dynamic 2D wind farm model in FRED [5,8] with two correction factors:…”

Section: Introductionmentioning

confidence: 99%

Flow Modelling for Wind Farm Control: 2D vs. 3D

Broek

Sanderse

Wingerden

2022

J. Phys.: Conf. Ser.

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Control-oriented models provide a basis for wind farm control to improve power production and reduce structural loading. Wake steering is considered to be one of the most promising techniques to achieve this. Wind turbine wakes under yaw misalignment are deflected downstream and have been shown to produce a curled or kidney-shaped structure. A Navier-Stokes based code called FRED was developed to model wind farm flow in 2D to perform yaw control. To tackle the differences between 2D and 3D flow, this work introduces a generalised continuity correction and wind turbine force scaling terms to the FRED framework. The effectiveness of approximating 3D results is tested by comparison with 3D simulations in the same framework. The continuity correction is now applicable to general wind directions and effective in reducing wake width and speed-up effects. The magnitude of wake deflection can be tuned using a force scaling term. However, we show that there remains a qualitative difference in the deflection profile downstream, as well as a difference in the propagation of yaw effects over time. From this study we can conclude that there is a fundamental difference between 2D and 3D flow physics in spatial and temporal dynamics which makes the 2D modelling approach challenging for control without further empirical adjustments. The necessary corrections are likely to be complex and non-physical, leading to a departure from the first principles foundation that FRED is developed from.

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Dynamic Flow Modelling for Model-Predictive Wind Farm Control

Cited by 8 publications

References 12 publications

FarmConners Wind Farm Flow Control Benchmark: Blind Test Results

FarmConners Wind Farm Flow Control Benchmark: Blind Test Results

Transient phenomena study in wind energy by means of LES simulations: impact of wind direction changes

Flow Modelling for Wind Farm Control: 2D vs. 3D

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