Monitoring of wind farms’ power curves using machine learning techniques

The estimation of a wind farm’s power curve, which links the wind speed to the power that is produced by the whole wind farm, is a challenging task because this relationship is nonlinear and bounded, in addition to being non-stationary due for example to changes in the site environment and seasonality. Even for a single wind turbine the measured power at different wind speeds is generally different than the rated power, since the operating conditions on site are generally different than the conditions under which the turbine was calibrated (the wind speed on site is not uniform horizontally across the face of the turbine; the vertical wind profile and the air density are different than during the calibration; the wind data available on site are not always measured at the height of the turbine’s hub).

[1]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[2]  Shuhui Li,et al.  Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation , 2001 .

[3]  G. S. Watson,et al.  Smooth regression analysis , 1964 .

[4]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[5]  Andrew Kusiak,et al.  Models for monitoring wind farm power , 2009 .

[6]  Lars Landberg,et al.  Short-term prediction of the power production from wind farms , 1999 .

[7]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[8]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[9]  Dimitri P. Solomatine,et al.  Machine learning in soil classification , 2005 .

[10]  Whei-Min Lin,et al.  Design of intelligent controllers for wind generation system with sensorless maximum wind energy control , 2011 .

[11]  Andrew Kusiak,et al.  Optimization of wind turbine energy and power factor with an evolutionary computation algorithm , 2010 .

[12]  Andrew Kusiak,et al.  On-line monitoring of power curves , 2009 .

[13]  Ruddy Blonbou,et al.  Very short-term wind power forecasting with neural networks and adaptive Bayesian learning , 2011 .

[14]  Whei-Min Lin,et al.  Fuzzy neural network output maximization control for sensorless wind energy conversion system , 2010 .

[15]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[16]  S. Van Huffel,et al.  A novel neural approach to inverse problems with discontinuities (the GMR neural network) , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[17]  Károly Tar,et al.  A statistical model for estimating electricity produced by wind energy , 2011 .

[18]  Kazuyuki Aihara,et al.  Complex-valued forecasting of wind profile , 2006 .

[19]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[20]  Maria Grazia De Giorgi,et al.  Error analysis of short term wind power prediction models , 2011 .

[21]  W. Gardner.  Introduction to Einstein's contribution to time-series analysis , 1987, IEEE ASSP Magazine.

[22]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[23]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[24]  Vladimir M. Krasnopolsky,et al.  A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components , 2006 .

[25]  Martin T. Hagan,et al.  Neural network design , 1995 .

[26]  Kazuyuki Aihara,et al.  Complex-valued prediction of wind profile using augmented complex statistics , 2009 .

[27]  Vladimir M. Krasnopolsky,et al.  Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction , 2006, Neural Networks.

[28]  Wei-Chiang Hong,et al.  Rainfall forecasting by technological machine learning models , 2008, Appl. Math. Comput..

[29]  G. Cirrincione,et al.  Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor , 2010 .

[30]  Gabriele Freni,et al.  Collection of Thermal Energy Available from a Biogas Plant for Leachate Treatment in an Urban Landfill: A Sicilian Case Study , 2012 .

[31]  E. Nadaraya.  On Estimating Regression , 1964 .

[32]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[33]  Taner Ustuntas,et al.  Wind turbine power curve estimation based on cluster center fuzzy logic modeling , 2008 .

[34]  Andrew Kusiak,et al.  Power optimization of wind turbines with data mining and evolutionary computation , 2010 .

[35]  Giansalvo Cirrincione,et al.  Wind speed spatial estimation for energy planning in Sicily: A neural kriging application , 2008 .

[36]  Joao P. S. Catalao,et al.  Short-term wind power forecasting in Portugal by neural networks and wavelet transform , 2011 .

[37]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[38]  Dimitri P. Solomatine,et al.  Machine learning in soil classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[39]  Antonio Messineo,et al.  Performance evaluation of hybrid RO/MEE systems powered by a WTE plant , 2008 .

[40]  Alfredo Vaccaro,et al.  An adaptive framework based on multi-model data fusion for one-day-ahead wind power forecasting , 2011 .

[41]  Huei-Lin Chang,et al.  Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs , 2010 .

[42]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[43]  Domenico Panno,et al.  Potential applications using LNG cold energy in Sicily , 2008 .

[44]  Kurt Stadlthanner,et al.  KPCA denoising and the pre-image problem revisited , 2008, Digit. Signal Process..

[45]  Ligia Tiruta-Barna,et al.  Combining lifecycle and risk assessments of mineral waste reuse scenarios for decision making support , 2007 .

[46]  Antonio Messineo,et al.  LNG cold energy use in agro-food industry: A case study in Sicily , 2011 .

[47]  Joao P. S. Catalao,et al.  A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal , 2011 .