Overview of preparation for the American WAKE ExperimeNt (AWAKEN)
Explore the source record for details and available documents.
54 ENVIRONMENTAL SCIENCES↗
Engineering topics
Scholbrock, Andrew
Publications and source records attributed to Scholbrock, Andrew.
Lessons Learned From the Planning of Recent Wind Energy Technologies Office Field Campaigns
The U.S. Department of Energy (DOE) Wind Energy Technology Office (WETO) has funded several wind-energy-focused field campaigns in recent years. While essential to advance our understanding of the interactions between atmosphere and wind turbines, the planning of such campaigns has presented several challenges to the institutions involved in these efforts. In this document, we focus on the planning phase of the field campaigns and summarize the lessons learned from the recent AWAKEN, RAAW, and WFIP-3 field projects. We cover a variety of aspects to offer a proposed methodological pipeline that we recommend is followed when future similar endeavors are planned.
17 WIND ENERGY↗
Characterization of wind turbine flow through nacelle-mounted lidars: a review
This article provides a comprehensive review of the most recent advances in the planning, execution, and analysis of inflow and wake measurements from nacelle-mounted wind Doppler lidars. Lidars installed on top of wind turbines provide a holistic view of the inflow and wake characteristics required to characterize and optimize wind turbine performance, carry out model validation and calibration, and aid in real-time control. The need to balance the enhanced capabilities and limitations of lidars compared to traditional anemometers inspired a broad variety of approaches for scan design and wind reconstruction, which we discuss in this review. We give particular emphasis to identifying common guidelines and gaps in the available literature with the aim of providing an exhaustive picture of the state-of-the-art techniques for reconstructing wind plant flow using nacelle-mounted lidars.
17 WIND ENERGY↗
Holistic scan optimization of nacelle-mounted lidars for inflow and wake characterization at the RAAW and AWAKEN field campaigns
In this article, we provide a methodological framework for designing the scanning strategies of nacelle-mounted scanning lidars for wind energy field experiments, and apply it at two major experimental field campaigns. For the Rotor Aerodynamics, Aeroelastics, and Wake project (RAAW), we leverage two scanning lidars on one turbine to characterize the incoming turbulence and the turbine wake. For the American WAKE experimeNt (AWAKEN), we use four scanning lidars on top of four turbines in a large wind power plant to investigate both individual wakes and wind-plant-scale flow features.
17 WIND ENERGY↗
NWTC WindCube Nacelle Lidar - pre-campaign / Raw Data
Test data from the National Wind Technology Center (NWTC) for the WindCube Nacelle Lidar. Includes both real-time and 10-minute average data.
17 WIND ENERGY↗
Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models
Abstract. Wake measurements of a scanning Doppler lidar mounted on the nacelle of a full-scale wind turbine during a wake-steering experiment were used for the characterization of the wake flow, the evaluation of the wake-steering set-up, and the validation of analytical wake models. Inflow-scanning Doppler lidars, a meteorological mast, and the supervisory control and data acquisition (SCADA) system of the wind turbine complemented the set-up. Results from the wake-scanning Doppler lidar showed an increase in the wake deflection with the yaw angle and that the wake deflection was not in all cases beneficial for the power output of a downstream turbine due to a bias of the inflow wind direction perceived by the yawed wind turbine and the wake-steering design implemented. Both observations could be reproduced with an analytical model that was initialized with the inflow measurements. Error propagation from the inflow measurements that were used as model input and the power coefficient of a waked wind turbine contributed significantly to the model uncertainty. Lastly, the span-wise cross section of the wake was strongly affected by wind veer, masking the effects of the yawed wind turbine on the wake cross sections.
17 WIND ENERGY↗
Longitudinal coherence and short-term wind speed prediction based on a nacelle-mounted Doppler lidar
The spatial structure of turbulence in atmospheric boundary layer flows is highly relevant to wind energy. In particular, wind turbine control strategies based on inflow preview measurements require knowledge of the longitudinal evolution of turbulent flow as it approaches the rotor. These upstream measurements are usually obtained with nacelle-mounted wind lidars. In contrast to traditional in situ anemometry, lidars collect measurements within a probe volume which varies in size depending on the technology of the commercial system being used. Here, we address two issues related to the use of wind lidar to measure the incoming flow to a wind turbine: (i) whether existing longitudinal coherence models can be used to predict flow at the rotor, based on measurements performed at a distance away from the rotor; and (ii) what effect probe-volume averaging has on the inflow predictions. These two questions are critical to the design and implementation of robust wind turbine control strategies. To address these questions, we perform field measurements and large-eddy simulations to determine which incoming flow structures can be readily predicted with existing coherence models, and which require additional corrections to account for lidar volumetric averaging effects. Results reveal that the wind turbine induction zone has a negligible impact on the longitudinal coherence and first-order turbulence quantities, such as the standard deviation of velocity fluctuations. However, the phase of the signal, from which advection time periods of the turbulent structures are derived, is affected by the rotor blockage effect.
17 WIND ENERGY↗
IEA Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering
Lidar-assisted control is a promising technology for reducing the levelized cost of energy from wind turbines, but quantifying its impact at the overall system level requires sophisticated systems engineering analysis and optimization frameworks. The joint workshop on Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering was held by the International Energy Agency Wind Task 32 (Lidar) and Task 37 (Systems Engineering) in October 2019 to address this challenge. This paper summarizes the outcome of the workshop and presents a road map for further research. The most promising applications of lidar-assisted control identified at the workshop and discussed here include 1) increasing annual energy production, 2) decreasing capital expenditure costs by reducing design loads, 3) extending turbine lifetime by reducing operating loads, and 4) enabling wind turbine class upgrades. For each application, we review the state of the art and highlight remaining research needs. Finally, we discuss strategies for addressing these research needs by conducting high-fidelity systems engineering optimizations.
17 WIND ENERGY↗
Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2
This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in Fleming et al. (2019). The authors implemented wake steering on two turbine pairs, and compared results with the latest FLORIS (FLOw Redirection and Induction in Steady State) model of wake steering, showing good agreement in overall energy increase. Further, although not the original intention of the study, we also used the results to detect the secondary steering phenomenon. Results show an overall reduction in wake losses of approximately 6.6 % for the regions of operation, which corresponds to achieving roughly half of the static optimal result.
17 WIND ENERGY↗