scholarly journals Approaches for predicting wind turbine hub-height turbulence metrics

2021 ◽  
Author(s):  
Hannah Livingston ◽  
Nicola Bodini ◽  
Julie K. Lundquist
Keyword(s):  
Wind Turbine ◽  
Control Strategies ◽  
Atmospheric Stability ◽  
Ground Level ◽  
Atmospheric Measurements ◽  
Large Variability ◽  
Near Surface ◽  
Energy Applications ◽  
Main Driver ◽  
Surface Measurements

Abstract. Hub-height turbulence is essential for a variety of wind energy applications, ranging from wind plant siting to wind turbine control strategies. Because deploying hub-height meteorological towers can be a challenge, alternative ways to estimate hub-height turbulence are desired. In this paper, we assess to what degree hub-height turbulence can be estimated via other hub-height variables or ground-level atmospheric measurements in complex terrain, using observations from three meteorological towers at the Perdigão and WFIP2 field campaigns. We find a large variability across the three considered towers when trying to model hub-height turbulence intensity (TI) and turbulence kinetic energy (TKE) from hub-height or near-surface measurements of either wind speed, TI, or TKE. Moreover, we find that based on the characteristics of the specific site, atmospheric stability and upwind fetch either determine a significant variability in hub-height turbulence or are not a main driver of the variability in hub-height TI and TKE. Our results highlight how hub-height turbulence is simultaneously sensitive to numerous different factors, so that no simple and universal relationship can be determined to vertically extrapolate turbulence from near-surface measurements, or model it from other hub-height variables when considering univariate relationships. We suggest that a multivariate approach should instead be considered, possibly leveraging the capabilities of machine learning nonlinear algorithms.

scholarly journals Wind Energy Meteorology: Insight into Wind Properties in the Turbine-Rotor Layer of the Atmosphere from High-Resolution Doppler Lidar

2013 ◽  
Vol 94 (6) ◽  
pp. 883-902 ◽  
Author(s):  
Robert M. Banta ◽  
Yelena L. Pichugina ◽  
Neil D. Kelley ◽  
R. Michael Hardesty ◽  
W. Alan Brewer
Keyword(s):  
Wind Energy ◽  
Ground Level ◽  
Turbine Rotor ◽  
Energy Industry ◽  
Doppler Lidar ◽  
Near Surface ◽  
Surface Measurements ◽  
Wind Measurements ◽  
Upper Level ◽  
Insight Into

Addressing the need for high-quality wind information aloft in the layer occupied by turbine rotors (~30–150 m above ground level) is one of many significant challenges facing the wind energy industry. Without wind measurements at heights within the rotor sweep of the turbines, characteristics of the flow in this layer are unknown for wind energy and modeling purposes. Since flow in this layer is often decoupled from the surface, near-surface measurements are prone to errant extrapolation to these heights, and the behavior of the near-surface winds may not reflect that of the upper-level flow.

scholarly journals Comparison of lidar-derived PM<sub>10</sub> with regional modeling and ground-based observations in the frame of MEGAPOLI experiment

2011 ◽  
Vol 11 (20) ◽  
pp. 10705-10726 ◽  
Author(s):  
P. Royer ◽  
P. Chazette ◽  
K. Sartelet ◽  
Q. J. Zhang ◽  
M. Beekmann ◽  
...  
Keyword(s):  
Mixed Boundary ◽  
Ground Level ◽  
In Situ Measurements ◽  
Percentage Error ◽  
Atmospheric Conditions ◽  
Transport Models ◽  
Near Surface ◽  
Lidar Measurements ◽  
Surface Measurements

Abstract. An innovative approach using mobile lidar measurements was implemented to test the performances of chemistry-transport models in simulating mass concentrations (PM10) predicted by chemistry-transport models. A ground-based mobile lidar (GBML) was deployed around Paris onboard a van during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer experiment in July 2009. The measurements performed with this Rayleigh-Mie lidar are converted into PM10 profiles using optical-to-mass relationships previously established from in situ measurements performed around Paris for urban and peri-urban aerosols. The method is described here and applied to the 10 measurements days (MD). MD of 1, 15, 16 and 26 July 2009, corresponding to different levels of pollution and atmospheric conditions, are analyzed here in more details. Lidar-derived PM10 are compared with results of simulations from POLYPHEMUS and CHIMERE chemistry-transport models (CTM) and with ground-based observations from the AIRPARIF network. GBML-derived and AIRPARIF in situ measurements have been found to be in good agreement with a mean Root Mean Square Error RMSE (and a Mean Absolute Percentage Error MAPE) of 7.2 μg m−3 (26.0%) and 8.8 μg m−3 (25.2%) with relationships assuming peri-urban and urban-type particles, respectively. The comparisons between CTMs and lidar at ~200 m height have shown that CTMs tend to underestimate wet PM10 concentrations as revealed by the mean wet PM10 observed during the 10 MD of 22.4, 20.0 and 17.5 μg m−3 for lidar with peri-urban relationship, and POLYPHEMUS and CHIMERE models, respectively. This leads to a RMSE (and a MAPE) of 6.4 μg m−3 (29.6%) and 6.4 μg m−3 (27.6%) when considering POLYPHEMUS and CHIMERE CTMs, respectively. Wet integrated PM10 computed (between the ground and 1 km above the ground level) from lidar, POLYPHEMUS and CHIMERE results have been compared and have shown similar results with a RMSE (and MAPE) of 6.3 mg m−2 (30.1%) and 5.2 mg m−2 (22.3%) with POLYPHEMUS and CHIMERE when comparing with lidar-derived PM10 with periurban relationship. The values are of the same order of magnitude than other comparisons realized in previous studies. The discrepancies observed between models and measured PM10 can be explained by difficulties to accurately model the background conditions, the positions and strengths of the plume, the vertical turbulent diffusion (as well as the limited vertical model resolutions) and chemical processes as the formation of secondary aerosols. The major advantage of using vertically resolved lidar observations in addition to surface concentrations is to overcome the problem of limited spatial representativity of surface measurements. Even for the case of a well-mixed boundary layer, vertical mixing is not complete, especially in the surface layer and near source regions. Also a bad estimation of the mixing layer height would introduce errors in simulated surface concentrations, which can be detected using lidar measurements. In addition, horizontal spatial representativity is larger for altitude integrated measurements than for surface measurements, because horizontal inhomogeneities occurring near surface sources are dampened.

scholarly journals A novel approach to surface reanalysis

2021 ◽  
Author(s):  
Sabrina Wahl ◽  
Clarissa Figura ◽  
Jan D. Keller
Keyword(s):  
Data Assimilation ◽  
Ground Level ◽  
Data Set ◽  
Near Surface ◽  
Wind Speeds ◽  
Energy Applications ◽  
Novel Approach ◽  
Uncertainty Estimates ◽  
Ensemble Transform Kalman Filter ◽  
Regional Reanalysis

&lt;p&gt;Reanalysis is a procedure to merge numerical model integrations and observations to obtain a synergetic representation of the past climatological state of a system, e.g., of the atmosphere. An alternative to running a full reanalysis scheme is a so-called surface reanalysis. Here, an existing reanalysis is used as prior information (for the near-surface state). This first guess is then corrected in a data assimilation step preferrably by applying observations not used in the original assimilation. In such a scheme, an additional downscaling is often performed to enhance the spatial representation of the surface reanalysis.&lt;/p&gt;&lt;p&gt;We present here the development of a new approach aiming to establish such a data set based on the COSMO-REA6 regional reanalysis of the Hans-Ertel-Centre and Deutscher Wetterdienst (DWD). The data assimilation step is based on the operational Local Ensemble Transform Kalman Filter (LETKF) of DWD. While the data assimilation is often performed univariately in such surface reanalysis schemes, here we apply it to various parameters at once thus conserving the covariances among the parameters and allowing for a consistent multivariate utilization of the data. Further, this reanalysis will not be restricted to the ground level and near-surface parameters. Instead, it will be extended to the lower part of the boundary layer aiming at an improved representation of wind speeds in wind turbine hub heights especially relevant for renewable energy applications. The envisaged resolution is approximately 1km and therefore enables an enhanced representation of spatial variability and heterogeneity on small scales. In addition, the LETKF is an ensemble-based data assimilation scheme which also provides uncertainty estimates through an ensemble of the re-analyzed parameters which can also be used as input for downstream applications.&lt;/p&gt;

scholarly journals Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

2020 ◽  
Vol 5 (2) ◽  
pp. 469-488
Author(s):  
James B. Duncan Jr. ◽  
Brian D. Hirth ◽  
John L. Schroeder
Keyword(s):  
Wind Turbine ◽  
Potential Benefit ◽  
Scale Validation ◽  
Control Strategies ◽  
Atmospheric Stability ◽  
Full Scale ◽  
Atmospheric Conditions ◽  
Turbine Wake ◽  
Plant Control ◽  
Wind Turbine Wake

Abstract. Recent research promotes implementing next-generation wind plant control methods to mitigate turbine-to-turbine wake effects. Numerical simulation and wind tunnel experiments have previously demonstrated the potential benefit of wind plant control for wind plant optimization, but full-scale validation of the wake-mitigating control strategies remains limited. As part of this study, the yaw and blade pitch of a utility-scale wind turbine were strategically modified for a limited time period to examine wind turbine wake response to first-order turbine control changes. Wind turbine wake response was measured using Texas Tech University's Ka-band Doppler radars and dual-Doppler scanning strategies. Results highlight some of the complexities associated with executing and analyzing wind plant control at full scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes, (2) identifying reliable data sources and methods to allow these control changes to be accurately quantified, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions (e.g., boundary layer streak orientation, atmospheric stability). To better understand wake sensitivity to the underlying atmospheric conditions, wake evolution within the early-evening transition was also examined using a single-Doppler data collection approach. Analysis of both wake length and meandering during this period of transitioning atmospheric stability indicates the potential benefit and feasibility of wind plant control should be enhanced when the atmosphere is stable.

scholarly journals Impacts of biogenic and anthropogenic emissions on summertime ozone formation in the Guanzhong Basin, China

2018 ◽  
Vol 18 (10) ◽  
pp. 7489-7507 ◽  
Author(s):  
Nan Li ◽  
Qingyang He ◽  
Jim Greenberg ◽  
Alex Guenther ◽  
Jingyi Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Control Strategies ◽  
Anthropogenic Sources ◽  
Air Pollution Control ◽  
Near Surface ◽  
Source Contributions ◽  
Surface Measurements ◽  
Individual Source ◽  
Guanzhong Basin ◽  
The Impact

Abstract. This study is the first attempt to understand the synergistic impact of anthropogenic and biogenic emissions on summertime ozone (O3) formation in the Guanzhong (GZ) Basin where Xi'an, the oldest and the most populous city (with a population of 9 million) in northwestern China, is located. Month-long (August 2011) WRF-Chem simulations with different sensitivity experiments were conducted and compared with near-surface measurements. Biogenic volatile organic compounds (VOCs) concentrations was characterized from six surface sites among the Qinling Mountains, and urban air composition was measured in Xi'an city at a tower 100 ma.s. The WRF-Chem control experiment reasonably reproduced the magnitudes and variations of observed O3, VOCs, NOx, PM2.5, and meteorological parameters, with normalized mean biases for each parameter within ±21 %. Subsequent analysis employed the factor separation approach (FSA) to quantitatively disentangle the pure and synergistic impacts of anthropogenic and/or biogenic sources on summertime O3 formation. The impact of anthropogenic sources alone was found to be dominant for O3 formation. Although anthropogenic particles reduced NO2 photolysis by up to 60 %, the anthropogenic sources contributed 19.1 ppb O3 formation on average for urban Xi'an. The abundant biogenic VOCs from the nearby forests promoted O3 formation in urban areas by interacting with the anthropogenic NOx. The calculated synergistic contribution (from both biogenic and anthropogenic sources) was up to 14.4 ppb in urban Xi'an, peaking in the afternoon. Our study reveals that the synergistic impact of individual source contributions to O3 formation should be considered in the formation of air pollution control strategies, especially for big cities in the vicinity of forests.

scholarly journals Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

2019 ◽  
Author(s):  
James B. Duncan Jr. ◽  
Brian D. Hirth ◽  
John L. Schroeder
Keyword(s):  
Wind Turbine ◽  
Potential Benefit ◽  
Scale Validation ◽  
Control Strategies ◽  
Atmospheric Stability ◽  
Full Scale ◽  
Atmospheric Conditions ◽  
Turbine Wake ◽  
Plant Control ◽  
Wind Turbine Wake

Abstract. Recent research promotes implementing next-generation wind plant control methods to mitigate turbine-to-turbine wake effects. Numerical simulation and wind tunnel experiments have previously demonstrated the potential benefit of wind plant control for wind plant optimization, but full-scale validation of the wake-mitigating control strategies remains limited. As part of this study, the yaw and blade pitch of a utility-scale wind turbine were strategically modified for a limited time period to examine wind turbine wake response to first-order turbine control changes. Wind turbine wake response was measured using Texas Tech University's Ka-band Doppler radars and dual-Doppler scanning strategies. Results highlight some of the complexities associated with executing and analysing wind plant control at full-scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes, (2) identifying reliable data sources and methods to allow these control changes to be accurately quantified, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions (e.g. boundary layer streak orientation, atmospheric stability). To better understand wake sensitivity to the underlying atmospheric conditions, wake evolution within the early-evening transition was also examined using a single-Doppler data collection approach. Analysis of both wake length and meandering during this period of transitioning atmospheric stability indicate the potential benefit and feasibility of wind plant control should be enhanced when the atmosphere is stable.

scholarly journals Aerosol Characterization during the Summer 2017 Huge Fire Event on Mount Vesuvius (Italy) by Remote Sensing and In Situ Observations

2021 ◽  
Vol 13 (10) ◽  
pp. 2001
Author(s):  
Antonella Boselli ◽  
Alessia Sannino ◽  
Mariagrazia D’Emilio ◽  
Xuan Wang ◽  
Salvatore Amoruso
Keyword(s):  
Remote Sensing ◽  
Biomass Burning ◽  
Ground Level ◽  
Large Area ◽  
Fire Event ◽  
Near Surface ◽  
Mean Values ◽  
Microphysical Properties ◽  
Observational Site ◽  
Biomass Burning Aerosol

During the summer of 2017, multiple huge fires occurred on Mount Vesuvius (Italy), dispersing a large quantity of ash in the surrounding area ensuing the burning of tens of hectares of Mediterranean scrub. The fires affected a very large area of the Vesuvius National Park and the smoke was driven by winds towards the city of Naples, causing daily peak values of particulate matter (PM) concentrations at ground level higher than the limit of the EU air quality directive. The smoke plume spreading over the area of Naples in this period was characterized by active (lidar) and passive (sun photometer) remote sensing as well as near-surface (optical particle counter) observational techniques. The measurements allowed us to follow both the PM variation at ground level and the vertical profile of fresh biomass burning aerosol as well as to analyze the optical and microphysical properties. The results evidenced the presence of a layer of fine mode aerosol with large mean values of optical depth (AOD > 0.25) and Ångstrom exponent (γ > 1.5) above the observational site. Moreover, the lidar ratio and aerosol linear depolarization obtained from the lidar observations were about 40 sr and 4%, respectively, consistent with the presence of biomass burning aerosol in the atmosphere.

scholarly journals Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

2021 ◽  
Vol 13 (11) ◽  
pp. 2121
Author(s):  
Changsuk Lee ◽  
Kyunghwa Lee ◽  
Sangmin Kim ◽  
Jinhyeok Yu ◽  
Seungtaek Jeong ◽  
...  
Keyword(s):  
Cross Validation ◽  
Ground Level ◽  
Reanalysis Data ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

Sign in / Sign up

Export Citation Format

Share Document