scholarly journals Development of a Low-Dimensional Wind Turbine Inflow Turbulence Model: Second Quarterly Report; December 15, 2002--March 15, 2003

2003 ◽  
Author(s):  
J. W. Naughton ◽  
W. R. Lindberg
Keyword(s):  
Wind Turbine ◽  
Turbulence Model ◽  
Inflow Turbulence ◽  
Low Dimensional

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.

Symmetry Considerations When Using Proper Orthogonal Decomposition for Predicting Wind Turbine Yaw Loads

2006 ◽  
Vol 128 (4) ◽  
pp. 574-579 ◽  
Author(s):  
Korn Saranyasoontorn ◽  
Lance Manuel
Keyword(s):  
Wind Turbine ◽  
Proper Orthogonal Decomposition ◽  
Random Fields ◽  
Orthogonal Decomposition ◽  
Extreme Statistics ◽  
Full Field ◽  
Monotonic Convergence ◽  
Inflow Turbulence ◽  
Low Dimensional ◽  
Proper Orthogonal

In an earlier study, the authors discussed the efficiency of low-dimensional representations of inflow turbulence random fields in predicting statistics of wind turbine loads that included blade and tower bending moments. Both root-mean-square and 10-min extreme statistics for these loads were approximated very well when time-domain simulations were carried out on a 600kW two-bladed turbine and only a limited number of inflow “modes” were employed using proper orthogonal decomposition (POD). Here, turbine yaw loads are considered and the conventional ordering of POD modes is seen to be not as efficient in predicting full-field load statistics for the same turbine. Based on symmetry arguments, reasons for a different treatment of yaw loads are presented and reasons for observed deviation from the expected monotonic convergence to full-field load statistics with increasing POD mode number are illustrated.

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.

A Review of Wind Turbine Polar Data and its Effect on Fatigue Loads Simulation Accuracy

2015 ◽  
Author(s):  
Matthew Lennie ◽  
Georgios Pechlivanoglou ◽  
David Marten ◽  
Christian Navid Nayeri ◽  
Oliver Paschereit
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Turbulence Models ◽  
Leading Edge ◽  
Sensitivity Study ◽  
Wind Turbine Blades ◽  
Simulation Code ◽  
Simulation Accuracy ◽  
Inflow Turbulence ◽  
Lift And Drag

To certify a Wind Turbine the standard processes set out by the GL guidelines and the IEC61400 demand a large number of simulations in order to justify the safe operation of the machine in all reasonably probable scenarios. The result of this rather demanding process is that the simulations rely on lower fidelity methods such as the Blade Element Momentum (BEM) method. The BEM method relies on a number of simplified inputs including the coefficient of lift and drag polar data (usually referred to as polars). These polars are usually either measured experimentally, generated using tools such as XFoil or, in some cases obtained using 2D CFD. It is typical to then modify these polars in order to make them suitable for aeroelastic simulations. Some of these modifications include 360° angle of attack extrapolation methods and polar modifications to account for 3D effects. Many of these modifications can be perceived to be a black art due to the manual selection of coefficients. The polars can misrepresent reality for many reasons, for example, inflow turbulence can affect measurements obtained in wind tunnels. Furthermore, on real wind turbine blades leading edge erosion can reduce performance. Simulated polars can even vary significantly due to the choice of turbulence models. Stack these effects on top of the uncertainties caused by yaw error, pitch error and dynamic stall and one can clearly see an operating environment hostile to accurate simulations. Colloquial evidence suggests that experienced designers would account for all of these sources of errors methodically, however, this is not reflected by the certification process. A review of experimental data and literature was performed to identify some of the inaccuracies in wind turbine polars. Significant variations were found between a range of 2D polar techniques and wind tunnel measurements. A sensitivity study was conducted using the aeroelastic simulation code FAST (National Renewable Energy Laboratory) with lift and drag polars sourced using different methods. The results were post-processed to give comparisons the rotor blade fatigue damage; variations in accumulated damages reached levels of 164%. This variation is not disastrous but is certainly enough to motivate a new approach for certifying the aerodynamic performance of wind turbines. Such an approach would simply see the source of polar data and all post-processing steps documented and included in the checks performed by certification bodies.

On the Modeling of Nonlinear Waves for Prediction of Long-Term Offshore Wind Turbine Loads

2008 ◽  
Author(s):  
P. Agarwal ◽  
L. Manuel
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Return Period ◽  
Wave Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Offshore Wind Turbines ◽  
Irregular Wave ◽  
Inflow Turbulence

In the design of wind turbines—onshore or offshore—the prediction of extreme loads associated with a target return period requires statistical extrapolation from available loads data. The data required for such extrapolation are obtained by stochastic time-domain simulation of the inflow turbulence, the incident waves, and the turbine response. Prediction of accurate loads depends on assumptions made in the simulation models employed. While for the wind, inflow turbulence models are relatively well established, for wave input, the current practice is to model irregular (random) waves using a linear wave theory. Such a wave model does not adequately represent waves in shallow waters where most offshore wind turbines are being sited. As an alternative to this less realistic wave model, the present study investigates the use of irregular nonlinear (second-order) waves for estimating loads on an offshore wind turbine, with a focus on the fore-aft tower bending moment at the mudline. We use a 5MW utility-scale wind turbine model for the simulations. Using, first, simpler linear irregular wave modeling assumptions, we establish long-term loads and identify governing environmental conditions (i.e., the wind speed and wave height) that are associated with the 20-year return period load derived using the inverse first-order reliability method. We present the nonlinear irregular wave model next and incorporate it into an integrated wind-wave-response simulation analysis program for offshore wind turbines. We compute turbine loads for the governing environmental conditions identified with the linear model and also for an extreme environmental state. We show that computed loads are generally larger with the nonlinear wave modeling assumptions; this establishes the importance of using such refined nonlinear wave models in stochastic simulation of the response of offshore wind turbines.

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