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Scholbrock, Andrew K.

Publications and source records attributed to Scholbrock, Andrew K..

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Initial Results From a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 1

Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. Results exploring the observed performance of wake steering are presented, as well as some first lessons learned. For two closely spaced turbines, an approximate 13 % increase in energy was measured on the downstream turbine over a 10° sector. Additionally, the increase of energy for the combined upstream/downstream pair was found to be in line with prior predictions. Lastly, the influence of atmospheric stability over the results is explored.

17 WIND ENERGY↗
DOE OSTI2018

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

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. Furthermore, these results show that good agreement is obtained between the lidar data and the models under these different conditions.

17 WIND ENERGY↗
DOE OSTI2018

Investigation into the shape of a wake of a yawed full-scale turbine

In this study, data from a lidar-based field campaign are used to examine the effect of yaw misalignment on the shape of a wind turbine wake. Prior investigation in wind tunnel research and high-fidelity computer simulation show that the shape assumes an increasingly curled shape as the wake propagates downstream, because of the presence of two counter-rotating vortices. The shape of the wake observed in the field data diverges from predictions of wake shape, and a lidar model is simulated within a large-eddy simulation of the wind turbine in the atmospheric boundary layer to understand the discrepancy.

17 WIND ENERGY↗
DOE OSTI2017

Evaluating Tilt for Wind Plants

The objective of this work is to demonstrate the feasibility of tilt in a wind plant. Tilt control, much like other wind plant control strategies, has the potential to improve the performance of a wind plant. Tilt control uses the tilt angle of the turbine to direct the wake above or below the downstream turbines. This paper presents a study of tilt in two- and three-turbine arrays. Specifically, the authors show that the power production of a two-turbine array can be increased by tilting turbines in a specific orientation. When adding more turbines, as is shown with the three-turbine array, the overall percentage of power gain increases. This outcome deviates from some of the results seen in typical wind plant control strategies. Finally, we discuss the impact this type of control strategy has on the aerodynamics in a wind plant. This analysis demonstrates that a good understanding of wake characteristics is necessary to improve the plant's performance. A tilt strategy such as the one presented in this paper may have implications for future control/optimization studies including optimization of hub heights in a wind plant and analysis of deep array effects.

17 WIND ENERGY↗
DOE OSTI2015

Field Testing of LIDAR-Assisted Feedforward Control Algorithms for Improved Speed Control and Fatigue Load Reduction on a 600-kW Wind Turbine: Preprint

A severe challenge in controlling wind turbines is ensuring controller performance in the presence of a stochastic and unknown wind field, relying on the response of the turbine to generate control actions. Recent technologies such as LIDAR, allow sensing of the wind field before it reaches the rotor. In this work a field-testing campaign to test LIDAR Assisted Control (LAC) has been undertaken on a 600-kW turbine using a fixed, five-beam LIDAR system. The campaign compared the performance of a baseline controller to four LACs with progressively lower levels of feedback using 35 hours of collected data.

17 WIND ENERGY↗
DOE OSTI2015

Analysis of axial‐induction‐based wind plant control using an engineering and a high‐order wind plant model

Abstract Wind turbines are typically operated to maximize their performance without considering the impact of wake effects on nearby turbines. Wind plant control concepts aim to increase overall wind plant performance by coordinating the operation of the turbines. This paper focuses on axial‐induction‐based wind plant control techniques, in which the generator torque or blade pitch degrees of freedom of the wind turbines are adjusted. The paper addresses discrepancies between a high‐order wind plant model and an engineering wind plant model. Changes in the engineering model are proposed to better capture the effects of axial‐induction‐based control shown in the high‐order model. Copyright © 2015 John Wiley & Sons, Ltd.

Annoni, Jennifer↗