Active Power Smoothing of Wind Generator Based on Wind Speed Prediction and Inverse-System

Abstract:

Active power smoothing of wind generator is very important to the utilization of wind energy. One-step ahead prediction of wind speed with the inverse-system is proposed in this paper. Wind generator is simplified as first order dynamic model. An inverse-system is designed to cascade with the model. Kalman filter and ARIMA models are used to predict the wind speed one-step ahead. The predicted wind speed is the input to the cascade system. The simulation shows that the fluctuations of the torque, the active power and the pitch angle are decreased effectively.

• Periodical: Advanced Materials Research (Volumes 383-390)
• Pages: 1363-1368
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.383-390.1363
• Online since: November 2011
• Authors: Xiao Lan Wang, Jiao Wu
• Keywords: Active Power Smoothing, Inverse-System, Variable-Pitch, Wind Generator, Wind Speed Prediction

References

[1] Geng Hua, Yang Geng, Inverse-system method for pitch conrol of a variable-speed variable-pitch wind generator, Journal of Tsinghua University (Sci&Tech), vol. 48, no. 7, pp.1221-1224, 2008(in Chinese).

DOI: 10.1109/pesc.2008.4592465

[2] Geng Hua, Yang Geng, Output power level control of variable-speed variable-pitch wind generators, Proceedings of the CSEE, vol. 28, no. 25, September 2008(in Chinese).

DOI: 10.1109/tec.2009.2034366

[3] Wang Xiao-lan, Li Hui, Effective wind speed forecasting in annual prediction of output power for wind farm, Proceedings of the CSEE, vol. 30, no. 8, March 2010(in Chinese).

[4] Senjyu T, Sakamoto R, Urasaki N, et al, Output power leveling of wind turbine generator for all operating regions by pitch angle control, IEEE. Transaction on Energy Conversion, vol. 21, no. 2, pp.467-475, June (2006).

DOI: 10.1109/tec.2006.874253

[5] Bianchi F. D, Mantz R. J, Christiansen C. F, Gain scheduling control of variable-speed wind energy conversion systems using quasi-LPV models, Control Engineering Practice, 2005, pp.247-255, doi: 10. 1016/j. conengprac. 2004. 03. 006.

DOI: 10.1016/j.conengprac.2004.03.006

[6] Ismael Sanchez, Short-term prediction of wind energy production, Inernational Journal of Forecasting, 2006, pp.43-56, doi: 10. 1016/ j. ijforecast. 2005. 05. 003.

[7] Mishra Y, Mishra S, Tripathy M, et al, Improving Stability of a DFIG-based wind power system with tuned damping controller, IEEE. Transaction on Energy Conversion, vol. 24, no. 3, September 2009, pp.650-660.

DOI: 10.1109/tec.2009.2016034

[8] Senjyu T,. Sakamoto R, Urasaki N, et al, Output power control of wind turbine generator by pitch angle control using minimum variance control, Electrical Engineering in Japan, vol. 154, no. 2, 2006, pp.1455-1463.

DOI: 10.1002/eej.20247

[9] Pan Di-fu, Liu Hui, Li Yan-fei, A wind speed forecasting optimization model for wind farms based on time series analysis and Kalman filter algorithm, Power System Technology, vol. 32, no. 7, April 2008(in Chinese).

[10] Yang Xiu-yuan, Xiao Yang, Chen Shu-yong, Wind speed and generated power forecasting in wind farm, Proceedings of the CSEE, vol. 25, no. 11, June 2005(in Chinese).

[11] Shao Fan, Sun Yu-he, Liang Lan-zhen, Wind speed short-term forecast for wind farms based on ARMA model, Power System and Clean Energy, vol. 24, no. 1, July 2008 (in Chinese).