Data Analytics Methods for Wind Energy Applications

In the wind industry, it is important to assess a turbine systems response under different wind profiles. For instance, a wind-to-power relationship is crucial for wind power forecast, and a wind-to-stress relationship is important for selecting critical design parameters meeting the reliability requirement. Given the complexity involved in a turbine system, it is impossible to write a neat, analytical expression to underlie the above-mentioned relationships. Almost invariably does the wind industry resort to data driven methods for a solution, namely that wind data and the corresponding turbine response data (bending moments or power outputs) are used together to fit empirically the functional relationship of interest. This paper presents a couple of nonparametric data analytic methods relevant to wind energy applications with real life example for demonstration.Copyright © 2015 by ASME

[1]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[2]  Refik Soyer,et al.  Bayesian Methods for Nonlinear Classification and Regression , 2004, Technometrics.

[3]  S. Hubbert.  Extreme Value Theory , 2019, Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management.

[4]  Gong Li,et al.  Application of Bayesian model averaging in modeling long-term wind speed distributions , 2010 .

[5]  Marc G. Genton,et al.  A kernel plus method for quantifying wind turbine performance upgrades , 2015 .

[6]  Gregor Giebel,et al.  The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview , 2003 .

[7]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[8]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[9]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[10]  Guohua Pan,et al.  Local Regression and Likelihood , 1999, Technometrics.

[11]  Puneet Agarwal,et al.  Extreme loads for an offshore wind turbine using statistical extrapolation from limited field data , 2008 .

[12]  Marc G. Genton,et al.  Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms , 2015 .

[13]  W. A. Timmer,et al.  Summary of the Delft University Wind Turbine Dedicated Airfoils , 2003 .

[14]  Eunshin Byon,et al.  BAYESIAN SPLINE METHOD FOR ASSESSING EXTREME LOADS ON WIND TURBINES , 2013, 1401.2760.