Wind Speed Forecast for Wind Farms Based on Phase Space Reconstruction of Wavelet Neural Network

Abstract:

Wind speed forecast is a non-linear and non-smooth problem. nonlinear and non-stationary are two kinds of mathematical problem, it is difficult to model with a single method, so that, a wavelet neural network model is set, the non-linear process of wind speed is forecast by neural networks and the non-stationary process of wind speed is decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transforms. wavelet combined with neural network model avoid the neural network model that can not handle non-stationary questions .while, the effect of indefinite inputs are removed by embedding dimension of phase space to determine neural networks inputs. The simulation results show that phase space reconstruction of wavelet neural network is more accuracy than the ordinary BP neural network. It could be well applied in wind speed forecasts.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 840-845
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.840
• Online since: January 2012
• Authors: Xiao Bing Xu, Jun He, Jian Ping Wang
• Keywords: Embedding Dimension, Phase, Space Reconstruction, Wavelet Neural Network (WNN)

References

[1] Yang Xiu-yuan, Xiao Yang, Chen Shuyong. Wind speed and generated power forecasting in wind farm [J] . Proceedings of the CSEE, 2005, 25(11): 1-5(in Chinese).

[2] Gu Xingkai, Fan Gaofeng, Wang Xiaorong, et al. Summarization of wind power prediction technology[J]. Power System Technology, 2007, 31(S2): 335-338(in Chinese).

[3] Ummels B C, Gibescu M, Pelgrum E, et al. Impacts of wind power on thermal generation unit commitment and dispatch [J] . IEEE Trans on Energy Conversion, 2007, 22 (1) : 44-51.

DOI: 10.1109/tec.2006.889616

[4] Pan Difu, Liu Hui, Li Yanfei. A wind speed forecasting optimization model for wind farms based on time series analysis and Kalman filter algorithm[J]. Power System Technology, 2008, 32(7): 82-86(in Chinese).

[5] Du Ying, Lu Jiping, Li Qing, et al. Short-term wind speed forecasting of wind farm based on least square-support vector machine[J]. Power System Technology, 2008, 32(15): 62-66(in Chinese).

[6] Sideratos G, Hatziargyriou N D. An advanced statistical method for wind power forecasting[J]. IEEE Trans on Power Systems, (2007).

DOI: 10.1109/tpwrs.2006.889078

[7] Cai Kai, Tan Lunnong, Li Chunlin, et al. Short-term wind speed forecasting combing time series and neural network method[J]. Power System Technology, 2008, 32(8): 82-85(in Chinese).

[8] Ding Ming, Zhang Lijun, Wu Yichun. Wind speed forecast model for wind farms based on time series analysis[J] . Electric Power Automation Equipment, 2005, 25(8): 32-34(in Chinese).

[9] Wu Xinghua, Zhou Hui, Huang Mei. Wind speed and generated power forecasting based on pattern recognition in wind farm Relay, 2008, 36(1): 27-32(in Chinese).

[10] Wen Quan, Zhang Yongchuan, Cheng Shijie. Chaotic time seriesanalysis to load prediction[J]. Power System Technology, 2001, 25(10): 13-16(in Chinese).

[11] Wang Lijie, Liao Xiaozhong, Gao Shuang, et al. Chaos characteristics analysis of wind power generation time series for a grid connecting wind farm[J]. Transactions of Beijing Institute of Technology, 2007, 27(12): 1077-1080(in Chinese).

[12] Takens F Mane. Detecting strange attractors in fluid turbulence [C]/ Dynamical Systems and Turbulence. Berlin: Springer, 1981: 21-80.

DOI: 10.1007/bfb0091924

[13] Yang Li-cai , Jia Lei , Kong Qing-jie Forecasting model using rough sets and orthogonal wavelet networks[J], School of Cont rol Science & Engineering , 2005, 27(8): 1463-1466.

[14] Chen Zhe, Feng Tian-jin, and Zhang Hai-yan chaotic time series analysis and phase space reconstruction based on wavelet neural network[J], journal of computer research &development 2001, 38(5): 591-596.

[15] Liu Zhi-gang, Wang Xiao-ru., Qian Qing-quan. Wavelet Network Research development and Application [J]. automation of electric power systems, 2003, 27(6): 73-79.

[16] Daubechies I Ten Lectures on Wavelets. Philadelphia, PA: SIAM Press, (1992).