Study on Probability Distribution of Wind Power Fluctuation Based on NACEMD and Improved Nonparametric Kernel Density Estimation

被引：0
|
作者：
Yang N. [1 ]
Huang Y. [1 ]
Ye D. [1 ]
Yan J. [2 ]
Zhang L. [1 ]
Dong B. [1 ]
机构：
[1] New Energy Micro-grid Collaborative Innovation Center of Hubei Province (China Three Gorges University), Yichang, 443002, Hubei
[2] State Grid Hubei Electric Power Economic Research Institute, Wuhan, 430000, Hubei
来源：
Dianwang Jishu/Power System Technology | 2019年 / 43卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Constrained ordinal optimization; Kernel density estimation; Signal decomposition; Wind power fluctuation characteristics;
D O I：
10.13335/j.1000-3673.pst.2018.0922
中图分类号：
学科分类号：
摘要：
For operation control process of large-scale wind farms, it is of great significance to accurately build probability distribution model of wind power fluctuation characteristics. According to the noise-assisted signal decomposition method based on complex empirical mode decomposition and adaptive nonparametric kernel density estimation method, a method of wind power fluctuation modeling is proposed. Firstly, the wind power is decomposed with the noise-assisted signal decomposition method based on complex empirical mode decomposition, and the fluctuation is extracted. Secondly, the probability characteristics are modeled based on upgraded nonparametric kernel density estimation and adaptively promoted based on the model. Finally, constrained ordinal optimization algorithm is utilized to solve the model. Accuracy and applicability of the modeling, and effectiveness of the model improvement are verified with simulation. © 2019, Power System Technology Press. All right reserved.
引用
收藏
页码：910 / 917
页数：7
相关论文
共 20 条
  • [1] Wang X., Li J., Huang B., Et al., A two-stage optimal dispatching model for provincial and regional power grids connected with wind farms to promote accommodation of wind power, Power System Technology, 39, 7, pp. 1833-1838, (2015)
  • [2] Zhuo J., Jin X., Deng B., Et al., An active power control method of large-scale wind farm considering security constraints of tie lines, Power System Technology, 39, 4, pp. 1014-1018, (2015)
  • [3] Jin Y., Ju P., Rehtanz C., Et al., Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm, Applied Energy, 222, pp. 485-496, (2018)
  • [4] Yan G., Wang Z., Li J., Et al., Research on output power fluctuation characteristics of the clustering Photovoltaic-wind joint power generation system based on continuous output analysis, International Conference on Power System Technology, pp. 2852-2857, (2014)
  • [5] Wang Y., Analysis on fluctuation characteristics of wind power, Advanced Materials Research, 989-994, pp. 1256-1259, (2014)
  • [6] Lin W., Wen J.Y., Ai X., Et al., Probability density function of wind power variations, Proceedings of the CSEE, 32, 1, (2012)
  • [7] Wan S., Wan J., Research on wind power fluctuation characteristics based on quantitative index and probability density distribution, ActaEnergiae Solaris Sinica, 36, 2, pp. 362-368, (2015)
  • [8] Cui Y., Yang H., Li H., Probability density distribution function of wind power fluctuation of a wind farm group based on the gaussian mixture model, Power System Technology, 40, 4, pp. 1107-1112, (2016)
  • [9] Lin H., Sun L., Chang X., A probabilistic model to simulate wind power output fluctuation of a certain wind farm cluster in Xinjiang region, Power System Technology, 38, 6, pp. 1616-1620, (2014)
  • [10] Yang M., Dong J., Study on characteristics of wind power fluctuation based on mixed distribution model, Proceedings of the CSEE, 36, pp. 69-78, (2016)
12