Abstract
A physical approach of the wind power prediction based on the CFD pre-calculated flow fields is proposed in this paper. The flow fields are obtained based on a steady CFD model with the discrete inflow wind conditions as the boundary conditions, and a database is established containing the important parameters including the inflow wind conditions, the flow fields and the corresponding wind power for each wind turbine. The power is predicted via the database by taking the Numerical Weather Prediction (NWP) wind as the input data. In order to evaluate the approach, the short-term wind power prediction for an actual wind farm is conducted as an example during the period of the year 2010. Compared with the measured power, the predicted results enjoy a high accuracy with the annual Root Mean Square Error (RMSE) of 15.2% and the annual MAE of 10.80%. A good performance is shown in predicting the wind power’s changing trend. This approach is independent of the historical data and can be widely used for all kinds of wind farms including the newly-built wind farms. At the same time, it does not take much computation time while it captures the local air flows more precisely by the CFD model. So it is especially practical for engineering projects.
References
KARINIOTAKIS G., MAYER D. and MOUSSAFIR J. Anemos: Development of a next generation wind power forecasting system for the large-scale integration of onshore and offshore wind farms[C]. European Wind Energy Conference and Exhibition Ewec 2003. Madrid, Spain, 2003.
WANG X., GUO P. and HUANG X. A review of wind power forecasting models[J]. Energy procedia, 2011, 12: 770–778.
KHALID M., SAVKIN A. V. A method for short-term wind power prediction with multiple observation points[J]. Ieee Transactions on Power Systems, 2012, 27(2): 579–586.
CATALAO J. P. S., POUSINHO H. M. I. and MENDES V. M. F. Short-term wind power forecasting in portugal by neural networks and wavelet transform[J]. Rene-wable Energy, 2011, 36(4): 1245–1251.
KNUDSEN T., BAK T. and SOLTANI M.Wind Energy, 2011, 14(7): 877–894.
BLONBOU R. Very short-term wind power forecasting with neural networks and adaptive Bayesian learning[J]. Renewable Energy, 2011, 36(3): 1118–1124.
WANG Li-jie, DONG Lei and LIAO Xiao-zhong et al. Short-term power prediction of a wind farm based on wavelet analysis[J], Proceedings of the Csee, 2009, 29(28): 30–33(in Chinese).
FAN Gao-feng, WANG Wei-sheng and LIU Chun et al. Wind power prediction based on artificial neural net-work[J]. Proceedings of the Csee, 2008, 28(34): 118–123(in Chinese).
LIU Yong-qian, HAN Shuang and YANG Yong-ping et al. Study on combined prediction of three hours in advance for wind power generation[J]. Acta Energlae Solaris Sinical, 2007, 28(8): 839–843(in Chinese).
LIU Y., SHI J. and YANG Y. et al. Piecewise support vector machine model for short term wind power predi-cition[J]. International Journal of Green Energy, 2009, 6(5): 479–489.
SHI Jie, LIU Yongqian and YANG Yongping et al. The rearch and application of wavelet support vector machine on short-term wind power prediction[C]. The 8th World Congress on Intelligent Control and Automation (Wcica 2010). Jinan, 2010.
LANDBERG L. Short-term prediction of local wind conditions[J]. Journal of Wind Engineering and In-dustrial Aerodynamics, 2001, 89(3–4): 235–245.
GIEBEL G., BADGER J. and MARTÍI P. l. Short-term forecasting using advanced physical modeling-the results of the Anemos project[C]. Proceedings of European Wind Energy Conference. Athens, Greece, 2006.
AL-DEEN S., YAMAGUCHI A. and ISHIHARA T. A physical approach to wind speed prediction for wind energy forecasting[C]. The Fourth International Symposium on Computational Wind Engineering. Yokohama, Japan, 2006.
FENG Shuang-lei, WANG Wei-sheng and LIU Chun et al. Study on the physical approach to wind power predi-ction[J]. Proceedings of the CSEE, 2010, 30(2): 1–6(in Chinese).
WANG Pei-fang, WANG Chao. Journal of Hydrodynamics, 2011, 23(2): 170–178.
VERMEER L. J., SØRENSEN J. N. and CRESPO A. Wind turbine wake aerodynamics[J]. Progress in Aerospace Sciences, 2003, 39(6–7): 467–510.
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Project supported by the National Natural Science Foundation of China (Grant No. 51206051).
Biography: LI Li (1974-), Female, Ph. D., Lecturer
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Li, L., Liu, Yq., Yang, Yp. et al. A physical approach of the short-term wind power prediction based on CFD pre-calculated flow fields. J Hydrodyn 25, 56–61 (2013). https://doi.org/10.1016/S1001-6058(13)60338-8
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DOI: https://doi.org/10.1016/S1001-6058(13)60338-8