A Evaluation Method of Wind Farm Power Prediction Based on Principal Component Analysis and Entropy Methods

被引：0

作者：

Yao Qi ^{[1
,2
]}

Liu Yu ^{[2
,4
]}

Bai Kai ^{[2
]}

Sun Rongfu ^{[3
]}

Song Peng ^{[2
]}

Wu Yuhui ^{[2
]}

机构：

[1] North China Elect Power Univ, Beijing 102206, Peoples R China

[2] North China Elect Power Res Inst Co Ltd, Beijing 100045, Peoples R China

[3] State Grid Jibei Elect Power Co, Beijing 100053, Peoples R China

[4] State Grid Wind Photovolta Energy Storage Hybrid, Beijing 100045, Peoples R China

来源：

PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年

关键词：

Wind Power Prediction; Evaluation Index; PCA; Entropy Method;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Wind power has been the most rapid developed new energy power form in recent years, its volatility, intermittent, and randomness have serious impact on the safe operation of power grid. Therefore the accurate prediction of the wind power is an important safeguard and reference to guide the new energy power system. Based on principal component analysis (PCA) and entropy method (EM), the paper expands the traditional single evaluation index and proposes a new comprehensive evaluation index. The results of the experiment show that this index is scientific and comprehensive, and can eliminate the human factor on index weight distribution.

引用

页码：2132 / 2136

页数：5

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