scholarly journals Low-dimensional representations and anisotropy of model rotor versus porous disk wind turbine arrays

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Elizabeth H. Camp ◽  
Raúl Bayoán Cal
Keyword(s):  
Wind Turbine ◽  
Porous Disk ◽  
Disk Wind ◽  
Low Dimensional ◽  
Turbine Arrays ◽  
Model Rotor

scholarly journals Wind Turbine Anomaly Detection Based on SCADA Data Mining

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 751
Author(s):  
Xiaoyuan Liu ◽  
Senxiang Lu ◽  
Yan Ren ◽  
Zhenning Wu
Keyword(s):  
Anomaly Detection ◽  
Wind Turbine ◽  
Supervisory Control ◽  
Detection Method ◽  
Classification Result ◽  
Detection Model ◽  
Scada System ◽  
Silhouette Coefficient ◽  
Abnormal State ◽  
Low Dimensional

In this paper, a wind turbine anomaly detection method based on a generalized feature extraction is proposed. Firstly, wind turbine (WT) attributes collected from the Supervisory Control And Data Acquisition (SCADA) system are clustered with k-means, and the Silhouette Coefficient (SC) is adopted to judge the effectiveness of clustering. Correlation between attributes within a class becomes larger, correlation between classes becomes smaller by clustering. Then, dimensions of attributes within classes are reduced based on t-Distributed-Stochastic Neighbor Embedding (t-SNE) so that the low-dimensional attributes can be more full and more concise in reflecting the WT attributes. Finally, the detection model is trained and the normal or abnormal state is detected by the classification result 0 or 1 respectively. Experiments consists of three cases with SCADA data demonstrate the effectiveness of the proposed method.

Low-Dimensional Representations of Inflow Turbulence and Wind Turbine Response Using Proper Orthogonal Decomposition

2005 ◽  
Vol 127 (4) ◽  
pp. 553-562 ◽  
Author(s):  
Korn Saranyasoontorn ◽  
Lance Manuel
Keyword(s):  
Wind Turbine ◽  
Proper Orthogonal Decomposition ◽  
Low Frequency ◽  
Orthogonal Decomposition ◽  
Response Variance ◽  
Spectral Models ◽  
Inflow Turbulence ◽  
Using Data ◽  
Low Dimensional ◽  
Proper Orthogonal

A demonstration of the use of Proper Orthogonal Decomposition (POD) is presented for the identification of energetic modes that characterize the spatial random field describing the inflow turbulence experienced by a wind turbine. POD techniques are efficient because a limited number of such modes can often describe the preferred turbulence spatial patterns and they can be empirically developed using data from spatial arrays of sensed input/excitation. In this study, for demonstration purposes, rather than use field data, POD modes are derived by employing the covariance matrix estimated from simulations of the spatial inflow turbulence field based on standard spectral models. The efficiency of the method in deriving reduced-order representations of the along-wind turbulence field is investigated by studying the rate of convergence (to total energy in the turbulence field) that results from the use of different numbers of POD modes, and by comparing the frequency content of reconstructed fields derived from the modes. The National Wind Technology Center’s Advanced Research Turbine (ART) is employed in the examples presented, where both inflow turbulence and turbine response are studied with low-order representations based on a limited number of inflow POD modes. Results suggest that a small number of energetic modes can recover the low-frequency energy in the inflow turbulence field as well as in the turbine response measures studied. At higher frequencies, a larger number of modes are required to accurately describe the inflow turbulence. Blade turbine response variance and extremes, however, can be approximated by a comparably smaller number of modes due to diminished influence of higher frequencies.

scholarly journals Design Methodology for a Floating Offshore Wind Turbine Large-Scale Outdoor Prototype

2019 ◽  
Author(s):  
Alessandro Fontanella ◽  
Federico Taruffi ◽  
Sara Muggiasca ◽  
Marco Belloli
Keyword(s):  
Wind Turbine ◽  
Large Scale ◽  
Offshore Wind ◽  
Elastic Response ◽  
Offshore Wind Turbine ◽  
Ocean Engineering ◽  
Floating Structure ◽  
Blue Growth ◽  
Turbine Model ◽  
Model Rotor

Abstract This paper discusses the methodology introduced by the authors to design a large-scale wind turbine model starting from the DTU 10MW RWT. The wind turbine will be coupled with the model of a multi-purpose floating structure, designed within the EU H2020 Blue Growth Farm project, and it will be deployed at the Natural Ocean Engineering Laboratory (NOEL). In this paper the different strategies used to design the wind turbine model rotor, tower and nacelle are discussed, focusing on how it has been possible to reproduce the full-scale system aero-elastic response while ensuring the same functionalities of a real wind turbine.

scholarly journals A Simulation Study on Risks to Wind Turbine Arrays from Thunderstorm Downbursts in Different Atmospheric Stability Conditions

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5407
Author(s):  
Nan-You Lu ◽  
Lance Manuel ◽  
Patrick Hawbecker ◽  
Sukanta Basu
Keyword(s):  
Boundary Layer ◽  
Wind Turbine ◽  
Transition Period ◽  
Atmospheric Stability ◽  
Bending Moment ◽  
Stability Conditions ◽  
Wind Fields ◽  
Worst Case ◽  
Large Eddy Simulation Model ◽  
Turbine Arrays

Thunderstorm downbursts have been reported to cause damage or failure to wind turbine arrays. We extend a large-eddy simulation model used in previous work to generate downburst-related inflow fields with a view toward defining correlated wind fields that all turbines in an array would experience together during a downburst. We are also interested in establishing what role contrasting atmospheric stability conditions can play on the structural demands on the turbines. This interest is because the evening transition period, when thunderstorms are most common, is also when there is generally acknowledged time-varying stability in the atmospheric boundary layer. Our results reveal that the structure of a downburst’s ring vortices and dissipation of its outflow play important roles in the separate inflow fields for turbines located at different parts of the array; these effects vary with stability. Interacting with the ambient winds, the outflow of a downburst is found to have greater impacts in an “average” sense on structural loads for turbines farther from the touchdown center in the stable cases. Worst-case analyses show that the largest extreme loads, although somewhat dependent on the specific structural load variable considered, depend on the location of the turbine and on the prevailing atmospheric stability. The results of our calculations show the highest simulated foreaft tower bending moment to be 85.4 MN-m, which occurs at a unit sited in the array farther from touchdown center of the downburst initiated in a stable boundary layer.

scholarly journals “Wind Theft” from Onshore Wind Turbine Arrays: Sensitivity to Wind Farm Parameterization and Resolution

2020 ◽  
Vol 59 (1) ◽  
pp. 153-174 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Patrick J. H. Volker ◽  
Andrea N. Hahmann ◽  
Rebecca J. Barthelmie
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Production ◽  
Horizontal Resolution ◽  
Spatial Extent ◽  
Wind Speeds ◽  
Stable Conditions ◽  
Wake Effects ◽  
Turbine Arrays

AbstractHigh-resolution simulations are conducted with the Weather Research and Forecasting Model to evaluate the sensitivity of wake effects and power production from two wind farm parameterizations [the commonly used Fitch scheme and the more recently developed Explicit Wake Parameterization (EWP)] to the resolution at which the model is applied. The simulations are conducted for a 9-month period for a domain encompassing much of the U.S. Midwest. The two horizontal resolutions considered are 4 km × 4 km and 2 km × 2 km grid cells, and the two vertical discretizations employ either 41 or 57 vertical layers (with the latter having double the number in the lowest 1 km). Higher wind speeds are observed close to the wind turbine hub height when a larger number of vertical layers are employed (12 in the lowest 200 m vs 6), which contributes to higher power production from both wind farm schemes. Differences in gross capacity factors for wind turbine power production from the two wind farm parameterizations and with resolution are most strongly manifest under stable conditions (i.e., at night). The spatial extent of wind farm wakes when defined as the area affected by velocity deficits near to wind turbine hub heights in excess of 2% of the simulation without wind turbines is considerably larger in simulations with the Fitch scheme. This spatial extent is generally reduced by increasing the horizontal resolution and/or increasing the number of vertical levels. These results have important applications to projections of expected annual energy production from new wind turbine arrays constructed in the wind shadow from existing wind farms.

Sign in / Sign up

Export Citation Format

Share Document