Power optimization of wind turbines with data mining and evolutionary computation

Renewable Energy

Volumn 35, Issue 3, 2010, Pages 695-702

  Kusiak, Andrew ^a   Zheng, Haiyang ^a   Song, Zhe ^a  

^a IA United States   (United States)

Author keywords

Data mining; Evolutionary computation algorithms; Neural networks; Optimization; Wind turbine

Indexed keywords

BLADE PITCH; COMPUTATIONAL STUDIES; CONTROL SETTINGS; DATA MINING ALGORITHM; DATA-DRIVEN APPROACH; EVOLUTIONARY COMPUTATION ALGORITHMS; EVOLUTIONARY COMPUTATIONS; EVOLUTIONARY STRATEGIES; FUNCTIONAL MAPPING; OPTIMAL CONTROLS; PITCH ANGLE; POWER OPTIMIZATION; POWER OUT PUT; TURBINE POWER; WIND FARM; YAW ANGLES;

CALCULATIONS; COMPUTATION THEORY; DATA MINING; ELECTRIC UTILITIES; NEURAL NETWORKS; OPTIMIZATION; THERMOELECTRIC POWER; TURBOMACHINE BLADES; WIND POWER; WIND TURBINES;

EVOLUTIONARY ALGORITHMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CONTROL SYSTEM; DATA MINING; OPTIMIZATION; POWER GENERATION; WIND FARM; WIND POWER; WIND TURBINE;

EID: 71549117932     PISSN: 09601481     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.renene.2009.08.018     Document Type: Article

References (30)

1. Bianchi F.D., De Battista H., and Mantz R.J. Wind turbine control systems: principles, modeling and gain scheduling design. 1st ed. (2006), Springer, London, UK p. 205
2. Bishop C. Neural networks for pattern recognition (1995), University Press, Oxford
3. Boukhezzar B., Siguerdidjane H., and Maureen Hand M. "Nonlinear control of variable-speed wind turbines for generator torque limiting and power optimization". ASME Transactions: Journal of Solar Energy Engineering 128 (2006) 516-530
4. Smola A.J., and Schoelkopf B. "A tutorial on support vector regression". Statistics and Computing vol. 14 (2004) 199-222
5. Breiman L., Friedman J.H., and Olshen R.A. Classification and regression trees (1984), Wadsworth International, Monterey, CA
6. Datta R., and Ranganathan V.T. "A method of tracking the peak power points for a variable speed wind energy conversion system". IEEE Transactions on Energy Conversion 18 (2003) 163-168
7. Eiben A.E., and Smith J.E. Introduction to evolutionary computation (2003), Springer-Verlag, New York
8. Friedman J.H. "Stochastic gradient boosting". Computational Statistics & Data Analysis 38 (2002) 367-378
9. Friedman J.H. "Greedy function approximation: a gradient boosting machine". Annals of Statistics 29 (2001) 1189-1232
10. Haykin S. Neural networks: a comprehensive foundation (1994), Macmillan Publishing, New York
11. Jangamshetti S.H., and Guruprasada Rau V. "Normalized power curves as a tool for identification of optimum wind turbine generator parameters". IEEE Transactions on Energy Conversion 16 (2001) 283-288
12. Kusiak A., and Song Z. "Combustion efficiency optimization and virtual testing: a data-mining approach". IEEE Transactions on Industrial Informatics 2 (2006) 176-184
13. Lakner M.A., and Elkinton C.N. "An analytical framework for offshore wind farm layout optimization". Wind Engineering 31 (2007) 17-31
14. Manwell J.F., McGowan J.G., and Rogers A.L. Wind energy explained: theory, design and application. 1st ed. (2002), John Wiley, London, UK
15. Morimoto S., Nakayama H., Sanada M., and Takeda Y. "Sensorless output maximization control for variable-speed wind generation system using IPMSG". IEEE Transactions on Industry Applications 41 (2005) 60-67
16. Muljadi E., and Butterfield C.P. "Pitch-controlled variable-speed wind turbine generation". IEEE Transactions on Industry Applications 37 (2001) 240-246
17. Munteanu I., Cutululis N.A., Bratcu A.I., and Ceanga E. "Optimization of variable speed wind power systems based on a LQG approach". Control Engineering Practice 13 (2005) 903-912
18. Song Y.D., Dhinakaran B., and Bao X.Y. "Variable speed control of wind turbines using nonlinear and adaptive algorithms". Journal of Wind Engineering and Industrial Aerodynamics 85 (2000) 293-308
19. Tan P.N., Steinbach M., and Kumar V. Introduction to data mining (2006), Pearson Education/Addison Wesley, Upper Saddle River, NJ
20. Walford C.A. Wind turbine reliability: understanding and minimizing wind turbine operation and maintenance costs. Available from: (2006), Sandia National Laboratoires, Albuquerque, NM. www.prod.sandia.gov/cgi-bin/techlib/access-control.pl/2006/061100.pdf
21. Wiser R., and Bolinger M. Annual report on U.S. wind power installation, cost, and performance trends: 2006. Available from: (2007), NREL, US Department of Energy, Golden, CO. http://www.nrel.gov/wind/pdfs/41435.pdf
22. Yurdusev M.A., Ata R., and Cetin N.S. "Assessment of optimum tip speed ratio in wind turbines using artificial neural networks". Energy 31 (2006) 2153-2161
23. http://www.awea.org.
24. Bernadett D.W. "Turbine class selection and layout design according to site conditions" (2007), Industrial Presentation, AWS Truewind
25. Kelouwani S., and Agbossou K. "Nonlinear model identification of wind turbine with a neural network". IEEE Transactions on Energy Conversion 19 (2004) 607-612
26. Shevade S.K., Keerthi S.S., Bhattacharyya C., and Murthy K.R.K. "Improvements to the SMO algorithm for SVM regression". IEEE Transactions on Neural Networks 11 (2000) 1188-1193
27. B. Bell. Individual wind turbine and overall power plant performance verification. San Diego, CA, 2008.
28. Graves A., and Harman K. Operating wind farm monitoring and performance optimization (2007) San Diego, CA
29. Horn R., and Johnson C. Topics in matrix analysis (1994), Cambridge University Press,, New York p. 607
30. Johnson K.E., Pao L.Y., Balas M.J., and Fingersh L.J. "Control of variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture". IEEE Control Systems Magazine 26 (2006) 70-81