Research on nonparametric kernel density estimation for modeling of wind power probability characteristics based on fuzzy ordinal optimization

被引：0
|
作者：
Yang N. [1 ]
Cui J. [1 ]
Zhou Z. [1 ]
Zhang S. [1 ]
Zhang L. [1 ]
Liu D. [2 ]
Hu W. [3 ]
机构：
[1] New Energy Micro-grid Collaborative Innovation Center of Hubei Province (China Three Gorges University), Yichang, 443000, Hubei Province
[2] School of Electrical Engineering, Wuhan University, Wuhan, 430072, Hubei Province
[3] Jingzhou Power Supply Bureau of State Grid, Jingzhou, 424002, Hubei Province
来源：
| 1600年 / Power System Technology Press卷 / 40期
基金：
中国国家自然科学基金;
关键词：
Membership function; Nonparametric kernel density estimation; Ordinal optimization; Probability density; Wind power;
D O I：
10.13335/j.1000-3673.pst.2016.02.001
中图分类号：
学科分类号：
摘要：
Study of wind power probability distribution model has important implications for wind farm planning and operation. This paper presented a non-parametric kernel density estimation method for modeling probability characteristics of wind power based on fuzzy distributed ordinal optimization. In this method, firstly, a non-parametric kernel density estimation model of wind power probability distribution was constructed by sampling wind power data. Then, a multi-objective optimization model was built for bandwidth selection. Finally, bandwidth optimization model was solved with fuzzy ordinal optimization. Numerical simulation results showed that the proposed modeling method was completely driven by the sample data, not requiring priori probability density model of subjective assumptions. Therefore, this model was more accurate and applicable. © 2016, Power System Technology Press. All right reserved.
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页码：335 / 340
页数：5
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