Virtual Models for Prediction of Wind Turbine Parameters

In this paper, a data-driven methodology for the development of virtual models of a wind turbine is presented. To demonstrate the proposed methodology, two parameters of the wind turbine have been selected for modeling, namely, power output and rotor speed. A virtual model for each of the two parameters is developed and tested with data collected at a wind farm. Both models consider controllable and noncontrollable parameters of the wind turbine, as well as the delay effect of wind speed and other parameters. To mitigate data bias of each virtual model and ensure its robustness, a training set is assembled from ten randomly selected turbines. The performance of a virtual model is largely determined by the input parameters selected and the data mining algorithms used to extract the model. Several data mining algorithms for parameter selection and model extraction are analyzed. The research presented in the paper is illustrated with computational results.

[1]  P. Dokopoulos,et al.  Short-term forecasting of wind speed and related electrical power , 1998 .

[2]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[3]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[4]  Chih-Fong Tsai,et al.  Feature selection in bankruptcy prediction , 2009, Knowl. Based Syst..

[5]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

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

[7]  Tim Coltman,et al.  Why build a customer relationship management capability? , 2007, J. Strateg. Inf. Syst..

[8]  Ulrich Focken,et al.  Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects , 2002 .

[9]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

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

[12]  Kemal Hanjalić,et al.  Sustainable energy technologies : options and prospects , 2008 .

[13]  Maureen Hand,et al.  Nonlinear Control of Variable-Speed Wind Turbines for Generator Torque Limiting and Power Optimization , 2006 .

[14]  Michael Negnevitsky,et al.  Innovative short-term wind generation prediction techniques , 2006 .

[15]  J.B. Theocharis,et al.  A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation , 2004, IEEE Transactions on Energy Conversion.

[16]  A. Kusiak,et al.  Short-Term Prediction of Wind Farm Power: A Data Mining Approach , 2009, IEEE Transactions on Energy Conversion.

[17]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[18]  Huo Xiao-ping,et al.  Study on the Time-series Wind Speed Forecasting of the Wind farm Based on Neural Networks , 2007 .

[19]  Edward R. Dougherty,et al.  Performance of feature-selection methods in the classification of high-dimension data , 2009, Pattern Recognit..

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

[21]  K. Agbossou,et al.  Nonlinear model identification of wind turbine with a neural network , 2004, IEEE Transactions on Energy Conversion.

[22]  Selwyn Piramuthu,et al.  Artificial Intelligence and Information Technology Evaluating feature selection methods for learning in data mining applications , 2004 .

[23]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

[24]  Andrew Kusiak,et al.  Data Mining in Manufacturing: A Review , 2006 .

[25]  P. Backus,et al.  Factory cycle-time prediction with a data-mining approach , 2006, IEEE Transactions on Semiconductor Manufacturing.

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

[27]  Eugene Fernandez,et al.  Analysis of wind power generation and prediction using ANN: A case study , 2008 .

[28]  J. Friedman.  Stochastic gradient boosting , 2002 .

[29]  Shuhui Li,et al.  Wind power prediction using recurrent multilayer perceptron neural networks , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[30]  Andrew Kusiak,et al.  Combustion efficiency optimization and virtual testing: a data-mining approach , 2006, IEEE Transactions on Industrial Informatics.

[31]  V. T. Ranganathan,et al.  A Method of Tracking the Peak Power Points for a Variable Speed Wind Energy Conversion System , 2002, IEEE Power Engineering Review.