Robust lidar-based closed-loop wake redirection for wind farm control

Raach, Steffen; Boersma, Sjoerd; Wingerden, Jan‐Willem van; Schlipf, David; Cheng, Po Wen

doi:10.1016/j.ifacol.2017.08.380

Cited by 9 publications

(16 citation statements)

References 12 publications

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“…If any of the nine points are outside of the wake, the weighted average may lead to a more conservative estimate of the flow in the wake. More details on the lidar used in the wake steering campaign can be found in Raach et al (2016Raach et al ( , 2017 and Fleming et al (2017a). The velocity computed by the scanning lidar is based on a line-of-sight velocity.…”

Section: Lidar Modelmentioning

confidence: 99%

Analysis of control-oriented wake modeling tools using lidar field results

et al. 2018

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Abstract. The objective of this paper is to compare field data from a scanning lidar mounted on a turbine to control-oriented wind turbine wake models. The measurements were taken from the turbine nacelle looking downstream at the turbine wake. This field campaign was used to validate control-oriented tools used for wind plant control and optimization. The National Wind Technology Center in Golden, CO, conducted a demonstration of wake steering on a utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment set points, while a lidar mounted on the nacelle scanned five downstream distances. Primarily, this paper examines measurements taken at 2.35 diameters downstream of the turbine. The lidar measurements were combined with turbine data and measurements of the inflow made by a highly instrumented meteorological mast on-site. This paper presents a quantitative analysis of the lidar data compared to the control-oriented wake models used under different atmospheric conditions and turbine operation. These results show that good agreement is obtained between the lidar data and the models under these different conditions.

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Section: Lidar Modelmentioning

confidence: 99%

Analysis of control-oriented wake modeling tools using lidar field results

et al. 2018

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“…Assumptions on flow, simplifications in energy extraction, and assuming symmetry in the wake simplify the calculations, however, always introduce uncertainties. Different control design approaches have been studied in Raach et al (2017c) and Raach et al (2017a). So far, the methodology had two main tasks: ensuring the tracking such that the wake is steered to the desired position and adapting to uncertainties 10 and disturbances.…”

Section: Challenges With Feedforward Wake Redirection Controlmentioning

confidence: 99%

Feedforward-Feedback wake redirection for wind farm control

Raach

Doekemeijer

Boersma

et al. 2019

Preprint

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This work presents a combined feedforward-feedback wake redirection framework for wind farm control. The FLORIS wake model, a control-oriented steady-state wake model is used to calculate optimal yaw angles for a given wind farm layout and atmospheric condition. The optimal yaw angles, which maximize the total power output, are applied to the wind farm. Further, the lidar-based closed-loop wake redirection concept is used to realize a local feedback on turbine level.The wake center is estimated from lidar measurements 3 D downwind of the wind turbines. The dynamical feedback controllers 5 support the feedforward controller and reject disturbances and adapt to model uncertainties. Altogether, the total framework is presented and applied to a nine turbine wind farm test case. In a high fidelity simulation study the concept shows promising results and an increase in total energy production compared to the baseline case and the feedforward-only case. IntroductionCurrently, wind farms are operating with individual optimal turbine settings thus not taking wake interactions into account. 10This strategy is referred to as greedy wind farm control. The two main wind farm control strategies in which wake interactions are taken into account are axial induction control and wake redirection control (see Boersma et al. (2017) for an overview).In the former, the idea is to deviate the blade pitch angle and tip speed ratio from greedy settings in order to enhance farm performance. Changing these control signals alters, among others, the wind velocity deficit in the turbine's wake hence the power production of downstream turbines. One of the first papers that proposes the idea of axial induction control can be 15 found in Steinbuch et al. (1988). By now, scientific results seem to indicate that by using a currently available steady-state model to evaluate optimal axial induction settings, no power improvement on a farm level can be achieved Annoni et al. (2018). However, recent scientific results indicate that by temporally changing axial induction settings, the farms power output in the therein used wind farm simulators can be improved by using control Ciri et al. (2017); Munters and Meyers (2018). Interestingly, the results in Ciri et al. (2017) seem to indicate that downstream turbine need to deviate from greedy in order 20 to improve the farm's power production while in Munters and Meyers (2018), the control settings of the upstream turbines are temporally changing resulting in an improvement of the farm's power output indicating the necessity for more research.The second wind farm control strategy is wake redirection control and studied in this paper. In this strategy, the goal is to 1 https://doi.

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“…Wake redirection is typically the more beneficial strategy in terms of power production increase potential. As such, it has received a lot of attention recently by the research community, focused on topics ranging from modeling the effects from yaw misalignment [10,11], to including loads into the optimization [12,13], to using flow-measurements in a feedback control setting [14]. Recently, NREL published the first measurement results from test with wake redirection on a few offshore wind turbines [15].…”

Section: Introductionmentioning

confidence: 99%

Dynamic wake steering and its impact on wind farm power production and yaw actuator duty

Kanev

2020

Renewable Energy

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Wake redirection is a wind farm control strategy that aims at increasing the overall power yield of a wind farm. It involves intentional misalignment of the rotors of upstream wind turbines with respect to the wind direction, thereby diverting their wakes aside from downstream turbines. The yaw misalignment angles are typically optimized using static wake models. In real-life, due to the rapid fluctuations of the wind direction with time, the optimized yaw misalignment angles cannot be instantaneously tracked as this would inevitably require an unacceptable amount of rotor yawing. This work is focused on how to dynamically adapt the statically optimzied yaw misalignment angles to achieve a good balance between high energy gain and limited yaw actuator duty cycle.

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Robust lidar-based closed-loop wake redirection for wind farm control

Cited by 9 publications

References 12 publications

Analysis of control-oriented wake modeling tools using lidar field results

Analysis of control-oriented wake modeling tools using lidar field results

Feedforward-Feedback wake redirection for wind farm control

Dynamic wake steering and its impact on wind farm power production and yaw actuator duty

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