Wind farm wake modeling for power prediction and detection of wildlife activity using X-band Doppler radar

2020 ◽  
Author(s):  
Jian Teng
Keyword(s):  
Wind Farm ◽  
Doppler Radar ◽  
Power Prediction ◽  
X Band ◽  
Wake Modeling

scholarly journals Design and analysis of a spatially heterogeneous wake

2020 ◽  
Author(s):  
Alayna Farrell ◽  
Jennifer King ◽  
Caroline Draxl ◽  
Rafael Mudafort ◽  
Nicholas Hamilton ◽  
...  
Keyword(s):  
Flow Field ◽  
Wind Farm ◽  
Wind Farms ◽  
Weather Conditions ◽  
Atmospheric Conditions ◽  
Power Prediction ◽  
Atmospheric Measurements ◽  
Flow Field Characteristics ◽  
Spatially Variant ◽  
Wake Modeling

Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the Supervisory Control And Data Acquisition (SCADA) recordings. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.

scholarly journals Design and analysis of a wake model for spatially heterogeneous flow

2021 ◽  
Vol 6 (3) ◽  
pp. 737-758
Author(s):  
Alayna Farrell ◽  
Jennifer King ◽  
Caroline Draxl ◽  
Rafael Mudafort ◽  
Nicholas Hamilton ◽  
...  
Keyword(s):  
Flow Field ◽  
Power Output ◽  
Wind Farm ◽  
Atmospheric Effects ◽  
Power Prediction ◽  
Atmospheric Measurements ◽  
Heterogeneous Model ◽  
Site Specific ◽  
Heterogeneous Flow ◽  
Wake Modeling

Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The objective of this method is to capture heterogeneous atmospheric effects caused by site-specific terrain features, without explicitly modeling the geometry of the wind farm terrain. The implemented adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the supervisory control and data acquisition (SCADA) recordings and large eddy simulations (LESs). When comparing the performance of the proposed heterogeneous model to homogeneous FLORIS simulations, the results show a 14.6 % decrease for mean absolute error (MAE) in wind farm power output predictions for cases using wind farm SCADA data and a 18.9 % decrease in LES case studies. The results of these studies also indicate that the efficacy of the proposed modeling techniques may vary with differing site-specific operational conditions. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.

The Multiple-Vortex Structure of the El Reno, Oklahoma, Tornado on 31 May 2013

2018 ◽  
Vol 146 (8) ◽  
pp. 2483-2502 ◽  
Author(s):  
Howard B. Bluestein ◽  
Kyle J. Thiem ◽  
Jeffrey C. Snyder ◽  
Jana B. Houser
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Rapid Scan ◽  
X Band ◽  
Close Range ◽  
Formation And Evolution ◽  
Using Data ◽  
Secondary Vortices ◽  
The Right ◽  
Gust Front

Abstract This study documents the formation and evolution of secondary vortices associated within a large, violent tornado in Oklahoma based on data from a close-range, mobile, polarimetric, rapid-scan, X-band Doppler radar. Secondary vortices were tracked relative to the parent circulation using data collected every 2 s. It was found that most long-lived vortices (those that could be tracked for ≥15 s) formed within the radius of maximum wind (RMW), mainly in the left-rear quadrant (with respect to parent tornado motion), passing around the center of the parent tornado and dissipating closer to the center in the right-forward and left-forward quadrants. Some secondary vortices persisted for at least 1 min. When a Burgers–Rott vortex is fit to the Doppler radar data, and the vortex is assumed to be axisymmetric, the secondary vortices propagated slowly against the mean azimuthal flow; if the vortex is not assumed to be axisymmetric as a result of a strong rear-flank gust front on one side of it, then the secondary vortices moved along approximately with the wind.

Control-Oriented Wind Farm Wake Modeling with Near Wake Performance and LES Validation

2020 ◽  
Author(s):  
Zhen Xie ◽  
Zhongwei Lin ◽  
Zhenyu Chen ◽  
Chuanxi Wang ◽  
Qingru Cui ◽  
...  
Keyword(s):  
Wind Farm ◽  
Near Wake ◽  
Wake Modeling

scholarly journals Simulation of X-Band Rainfall Observations from S-Band Radar Data

2006 ◽  
Vol 23 (9) ◽  
pp. 1195-1205 ◽  
Author(s):  
V. Chandrasekar ◽  
S. Lim ◽  
E. Gorgucci
Keyword(s):  
Doppler Radar ◽  
Theoretical Models ◽  
Radar Data ◽  
Shape Distribution ◽  
Microphysical Properties ◽  
Rain Medium ◽  
Microphysical Parameters ◽  
X Band ◽  
State Water ◽  
The Impact

Abstract To design X-band radar systems as well as evaluate algorithm development, it is useful to have simultaneous X-band observation with and without the impact of path attenuation. One way to develop that dataset is through theoretical models. This paper presents a methodology to generate realistic range profiles of radar variables at attenuating frequencies, such as X band, for rain medium. Fundamental microphysical properties of precipitation, namely, size and shape distribution information, are used to generate realistic profiles of X band starting with S-band observation. Conditioning the simulation from S band maintains the natural distribution of rainfall microphysical parameters. Data from the Colorado State University’s University of Chicago–Illinois State Water Survey (CHILL) radar and the National Center for Atmospheric Research S-band dual-polarization Doppler radar (S-POL) are used to simulate X-band radar variables. Three procedures to simulate the radar variables and sample applications are presented.

Mobile X-Pol Radar: A New Tool for Investigating Pyroconvection and Associated Wildfire Meteorology

2018 ◽  
Vol 99 (6) ◽  
pp. 1177-1195 ◽  
Author(s):  
Nicholas McCarthy ◽  
Hamish McGowan ◽  
Adrien Guyot ◽  
Andrew Dowdy
Keyword(s):  
Multispectral Imaging ◽  
Doppler Radar ◽  
Fire Behavior ◽  
Time Lapse ◽  
Fire Intensity ◽  
Prescribed Burn ◽  
X Band ◽  
Turbulent Characteristics ◽  
Convective Plume ◽  
Dual Polarized

AbstractThe process of pyroconvection occurs when fire-released heat, moisture, and/or aerosols induce or augment convection in the atmosphere. Prediction of pyroconvection presents a set of complex problems for meteorologists and wildfire managers. In particular, the turbulent characteristics of a pyroconvective plume exert bidirectional feedback on fire behavior, often with resulting severe impacts on life and property. Here, we present the motivation, field strategy, and initial results from the Bushfire Convective Plume Experiment, which through the use of mobile radar aims to quantify the kinematics of pyroconvection and its role in fire behavior. The case studies presented include world-first observations from two wildfires and one prescribed burn using the University of Queensland’s portable, dual-polarized X-band Doppler radar (UQ-XPOL). The initial analyses of reflectivity, Doppler winds, polarimetric variables, and spectrum width data provide insights into these relatively unexplored datasets within the context of pyroconvection. Weather radar data are supported by mesonet observations, time-lapse photography, airborne multispectral imaging, and spot-fire mapping. The ability to combine ground-validated fire intensity and progression at an hourly scale with quantitative data documenting the evolution of the convective plume kinematics at the scale of hundreds of meters represents a new capability for advancing our understanding of wildfires. The results demonstrate the suitability of portable, dual-polarized X-band Doppler radar to investigate pyroconvection and associated plume dynamics.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

High-Temporal Resolution Polarimetric X-Band Doppler Radar Observations of the 20 May 2013 Moore, Oklahoma, Tornado

2015 ◽  
Vol 143 (7) ◽  
pp. 2711-2735 ◽  
Author(s):  
James M. Kurdzo ◽  
David J. Bodine ◽  
Boon Leng Cheong ◽  
Robert D. Palmer
Keyword(s):  
Temporal Resolution ◽  
Doppler Radar ◽  
Medical Center ◽  
Small Scale ◽  
High Temporal Resolution ◽  
Radar Observations ◽  
University Of Oklahoma ◽  
X Band ◽  
Damage Indicators ◽  
Gust Front

Abstract On 20 May 2013, the cities of Newcastle, Oklahoma City, and Moore, Oklahoma, were impacted by a long-track violent tornado that was rated as an EF5 on the enhanced Fujita scale by the National Weather Service. Despite a relatively sustained long track, damage surveys revealed a number of small-scale damage indicators that hinted at storm-scale processes that occurred over short time periods. The University of Oklahoma (OU) Advanced Radar Research Center’s PX-1000 transportable, polarimetric, X-band weather radar was operating in a single-elevation PPI scanning strategy at the OU Westheimer airport throughout the duration of the tornado, collecting high spatial and temporal resolution polarimetric data every 20 s at ranges as close as 10 km and heights below 500 m AGL. This dataset contains the only known polarimetric radar observations of the Moore tornado at such high temporal resolution, providing the opportunity to analyze and study finescale phenomena occurring on rapid time scales. Analysis is presented of a series of debris ejections and rear-flank gust front surges that both preceded and followed a loop of the tornado as it weakened over the Moore Medical Center before rapidly accelerating and restrengthening to the east. The gust front structure, debris characteristics, and differential reflectivity arc breakdown are explored as evidence for a “failed occlusion” hypothesis. Observations are supported by rigorous hand analysis of critical storm attributes, including tornado track relative to the damage survey, sudden track shifts, and a directional debris ejection analysis. A conceptual description and illustration of the suspected failed occlusion process is provided, and its implications are discussed.

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