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.
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页码：910 / 917
页数：7
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