scholarly journals Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

2021 ◽  
Vol 13 (21) ◽  
pp. 4438
Author(s):  
Jeanie A. Aird ◽  
Eliot W. Quon ◽  
Rebecca J. Barthelmie ◽  
Mithu Debnath ◽  
Paula Doubrawa ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Wind Turbine ◽  
Complex Terrain ◽  
Wind Farm ◽  
Test Model ◽  
Data Quality Control ◽  
Turbine Wake ◽  
Wake Characteristics ◽  
Wind Turbine Wake

We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can capture wake characteristics in spatially heterogeneous fields resulting from data quality control procedures and complex background flow fields. The geometry, spatial extent and locations of wakes and wake fragments exhibit close accord with results from visual inspection. The model exhibits a 95% success rate in identifying wakes when they are present in scans and characterizing their shape. To test model robustness to varying image quality, we reduced the scan density to half the original resolution through down-sampling range gates. This causes a reduction in skill, yet 92% of wakes are still successfully identified. When grouping scans by meteorological conditions and utilizing the CNN for wake characterization under full and half resolution, wake characteristics are consistent with a priori expectations for wake behavior in different inflow and stability conditions.

scholarly journals Utility-Scale Wind Turbine Wake Characterization Using Nacelle-Based Long-Range Scanning Lidar

2014 ◽  
Vol 31 (7) ◽  
pp. 1529-1539 ◽  
Author(s):  
Matthew L. Aitken ◽  
Julie K. Lundquist
Keyword(s):  
Wind Turbine ◽  
Long Range ◽  
Wind Farm ◽  
Thrust Coefficient ◽  
Velocity Deficit ◽  
Scanning Lidar ◽  
Range Scanning ◽  
Utility Scale ◽  
Turbine Wake ◽  
Wind Turbine Wake

Abstract To facilitate the optimization of turbine spacing at modern wind farms, computational simulations of wake effects must be validated through comparison with full-scale field measurements of wakes from utility-scale turbines operating in the real atmosphere. Scanning remote sensors are particularly well suited for this objective, as they can sample wind fields over large areas at high temporal and spatial resolutions. Although ground-based systems are useful, the vantage point from the nacelle is favorable in that scans can more consistently transect the central part of the wake. To the best of the authors’ knowledge, the work described here represents the first analysis in the published literature of a utility-scale wind turbine wake using nacelle-based long-range scanning lidar. The results presented are of a field experiment conducted in the fall of 2011 at a wind farm in the western United States, quantifying wake attributes such as the velocity deficit, centerline location, and wake width. Notable findings include a high average velocity deficit, decreasing from 60% at a downwind distance x of 1.8 rotor diameters (D) to 40% at x = 6D, resulting from a low average wind speed and therefore a high average turbine thrust coefficient. Moreover, the wake width was measured to expand from 1.5D at x = 1.8D to 2.5D at x = 6D. Both the wake growth rate and the amplitude of wake meandering were observed to be greater for high ambient turbulence intensity and daytime conditions as compared to low turbulence and nocturnal conditions.

scholarly journals FLOWSTAR-Energy: a high resolution wind farm wake model

2016 ◽  
Author(s):  
Amy Stidworthy ◽  
David Carruthers
Keyword(s):  
Boundary Layer ◽  
Wind Turbine ◽  
Complex Terrain ◽  
Wind Farm ◽  
Wind Farms ◽  
Measured Data ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wake Model ◽  
Turbine Wake

Abstract. A new model, FLOWSTAR-Energy, has been developed for the practical calculation of wind farm energy production. It includes a semi-analytic model for airflow over complex surfaces (FLOWSTAR) and a wind turbine wake model that simulates wake-wake interaction by exploiting some similarities between the decay of a wind turbine wake and the dispersion of plume of passive gas emitted from an elevated source. Additional turbulence due to the wind shear at the wake edge is included and the assumption is made that wind turbines are only affected by wakes from upstream wind turbines. The model takes account of the structure of the atmospheric boundary layer, which means that the effect of atmospheric stability is included. A marine boundary layer scheme is also included to enable offshore as well as onshore sites to be modelled. FLOWSTAR-Energy has been used to model three different wind farms and the predicted energy output compared with measured data. Maps of wind speed and turbulence have also been calculated for two of the wind farms. The Tjaæreborg wind farm is an onshore site consisting of a single 2 MW wind turbine, the NoordZee offshore wind farm consists of 36 V90 VESTAS 3 MW turbines and the Nysted offshore wind farm consists of 72 Bonus 2.3 MW turbines. The NoordZee and Nysted measurement datasets include stability distribution data, which was included in the modelling. Of the two offshore wind farm datasets, the Noordzee dataset focuses on a single 5-degree wind direction sector and therefore only represents a limited number of measurements (1,284); whereas the Nysted dataset captures data for seven 5-degree wind direction sectors and represents a larger number of measurements (84,363). The best agreement between modelled and measured data was obtained with the Nysted dataset, with high correlation (0.98 or above) and low normalised mean square error (0.007 or below) for all three flow cases. The results from Tjæreborg show that the model replicates the Gaussian shape of the wake deficit two turbine diameters downstream of the turbine, but the lack of stability information in this dataset makes it difficult to draw conclusions about model performance. One of the key strengths of FLOWSTAR-Energy is its ability to model the effects of complex terrain on the airflow. However, although the airflow model has been previously compared extensively with flow data, it has so far not been used in detail to predict energy yields from wind farms in complex terrain. This will be the subject of a further validation study for FLOWSTAR-Energy.

Characterizing the Transitional Behavior of Wind Turbine Wake From Near to Far Wake Regimes

2021 ◽  
Author(s):  
Ravi Kumar ◽  
Ojing Siram ◽  
Niranjan Sahoo ◽  
Ujjwal K. Saha
Keyword(s):  
Wind Turbine ◽  
Energy Demand ◽  
Wind Farm ◽  
Flow Characteristics ◽  
Wind Farms ◽  
Horizontal Axis Wind Turbine ◽  
Velocity Deficit ◽  
Turbine Wake ◽  
Far Wake ◽  
Wake Characteristics

Abstract Knowledge of wind energy harvesting is an ever-growing process, and to meet the enormous energy demand, wind farms shall have a significant role. An efficient wind farm is required to have an in-depth knowledge of turbine wake characteristics. This article presents an experimental investigation of the wake expansion process defined by the transition of wake from near to far wake regimes. The study has been performed on models horizontal axis wind turbine (HAWT) composed of NACA 0012 profile, keeping the ratio of root chord to tip chord length is 5:2. A constant temperature hot-wire anemometer (HWA) has been used to examine the rotor’s fluctuating flow field. The subsequent time-averaged normalizes velocity deficit, and vortex shedding frequency are used for the flow characteristics. Time-averaged velocity deficit measurement suggests a drop in upstream velocity by 20–30% within the vicinity of rotor tip downstream of the rotor plane. The study shows that flow recovery is initiating from the near wake regime around 1.08R. Further, the spectral findings indicates the low frequency dominance within 4R (R being the rotor radius), and the Strouhal number falls close to 0.23. The present wind tunnel study on wake characteristics throws significant insight into further enhancing the WT wake modeling.

scholarly journals Analysis of rotorcraft wind turbine wake encounters using piloted simulation

2021 ◽  
Author(s):  
Alexander Štrbac ◽  
Tanja Martini ◽  
Daniel H. Greiwe ◽  
Frauke Hoffmann ◽  
Michael Jones
Keyword(s):  
Wind Turbine ◽  
Alternative Energy ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Vortex Wake ◽  
Offshore Wind Farms ◽  
Turbine Wake ◽  
Wind Turbine Wake

AbstractThe use of offshore wind farms in Europe to provide a sustainable alternative energy source is now considered normal. Particularly in the North Sea, a large number of wind farms exist with a significant distance from the coast. This is becoming standard practice as larger areas are required to support operations. Efficient transport and monitoring of these wind farms can only be conducted using helicopters. As wind turbines continue to grow in size, there is a need to continuously update operational requirements for these helicopters, to ensure safe operations. This study assesses German regulations for flight corridors within offshore wind farms. A semi-empirical wind turbine wake model is used to generate velocity data for the research flight simulator AVES. The reference offshore wind turbine NREL 5 MW has been used and scaled to represent wind turbine of different sizes. This paper reports result from a simulation study concerning vortex wake encounter during offshore operations. The results have been obtained through piloted simulation for a transport case through a wind farm. Both subjective and objective measures are used to assess the severity of vortex wake encounters.

scholarly journals Meso- to micro-scale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain

2021 ◽  
Author(s):  
Adam S. Wise ◽  
James M. T. Neher ◽  
Robert S. Arthur ◽  
Jeffrey D. Mirocha ◽  
Julie K. Lundquist ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Complex Terrain ◽  
Recirculation Zone ◽  
Atmospheric Stability ◽  
Mountain Wave ◽  
Stable Case ◽  
Flow Phenomena ◽  
Turbine Wake ◽  
Good Agreement ◽  
Wind Turbine Wake

Abstract. Most detailed modeling and simulation studies of wind turbine wakes have considered flat terrain scenarios. Wind turbines, however, are commonly sited in mountainous or hilly terrain to take advantage of accelerating flow over ridgelines. In addition to topographic acceleration, other turbulent flow phenomena commonly occur in complex terrain, and often depend upon the thermal stratification of the atmospheric boundary layer. Enhanced understanding of wind turbine wake interaction with these terrain-induced flow phenomena can significantly improve wind farm siting, optimization, and control. In this study, we simulate conditions observed during the Perdigão field campaign in 2017, consisting of flow over two parallel ridges with a wind turbine located on top of one of the ridges. We use the Weather Research and Forecasting model (WRF) nested down to micro-scale large-eddy simulation (LES) at 10 m resolution, with a generalized actuator disk (GAD) wind turbine parameterization to simulate turbine wakes. Two case studies are selected, a stable case where a mountain wave occurs and a convective case where a recirculation zone forms in the lee of the ridge with the turbine. The WRF-LES-GAD model is validated against data from meteorological towers, soundings, and a tethered lifting system, showing good agreement for both cases. Comparisons with scanning Doppler lidar data for the stable case show that the overall characteristics of the mountain wave are well-captured, although the wind speed is underestimated. For the convective case, the size of the recirculation zone within the valley shows good agreement. The wind turbine wake behavior shows dependence on atmospheric stability, with different amounts of vertical deflection from the terrain and persistence downstream for the stable and convective conditions. For the stable case, the wake follows the terrain along with the mountain wave and deflects downwards by nearly 100 m below hub-height at four rotor diameters downstream. For the convective case, the wake deflects above the recirculation zone over 50 m above hub-height at the same downstream distance. This study demonstrates the ability of the WRF-LES-GAD model to capture the expected behavior of wind turbine wakes in regions of complex terrain, and thereby to potentially improve wind turbine siting and operation in hilly landscapes.

scholarly journals Automated Wind Turbine Wake Characterization in Complex Terrain

2019 ◽  
Author(s):  
Rebecca J. Barthelmie ◽  
Sara C. Pryor
Keyword(s):  
Wind Turbine ◽  
Complex Terrain ◽  
Atmospheric Stability ◽  
Average Height ◽  
Wind Speeds ◽  
Stable Conditions ◽  
The Mean ◽  
Low Wind Speeds ◽  
Turbine Wake ◽  
Wind Turbine Wake

Abstract. An automated wind turbine wake characterization algorithm has been developed and applied to a dataset of over 19,000 scans measured by scanning Doppler lidar at Perdigão over the period January to June 2017. The algorithm correctly identifies the wake centre position in 62 % of possible wake cases, 46 % having a clear and well-defined wake centre while 16 % have split centres or multiple lobes. Only 5 % of cases are not detected, the remaining 33 % could not be categorized either by the algorithm or subjectively, mainly due to the complexity of the background flow. Average wake centre heights categorized by inflow wind speeds are shown to be initially lofted (to 2 rotor diameters, D) except when the inflow wind speeds exceed 12 ms−1. Even under low wind speeds, by 3.5 D downstream of the wind turbine, the mean wake centre position is below the initial wind turbine hub-height and descends broadly following the terrain slope. However, this behaviour is strongly linked to hour of the day and atmospheric stability. Overnight and in stable conditions the average height of the wake centre is 10 m higher than in unstable conditions at 2 D and 17 m higher at 4.5 D downstream of the wind turbine.

Sign in / Sign up

Export Citation Format

Share Document