电力系统及其自动化学报2016，Vol.28Issue(11)：32-36,5.DOI:10.3969/j.issn.1003-8930.2016.11.006
RBF神经网络在风电场年度发电量估算中的应用
Application of Radial Basis Function Neural Network to Annual Energy Output Estimation of Wind Power Plant
摘要
Abstract
Estimation of wind power generation is the key step in turbine-site matching and wind power cost analysis. In order to estimate the wind power accurately,radial basis function(RBF)neural network is used to estimate the energy output of wind power plant. The modeling is based on the historical data from 18 meteorological stations of Chinese Tai⁃wan and 26 meteorological stations of Republic of Korea,then their energy outputs are estimated by themselves and among each other. Because the wind speed and generation hours are the main factors affecting wind power,this paper takes average annual wind speed and the k parameter of Weibull distribution which affects the annual generation hours as inputs. The estimated results are compared with actual wind power outputs,and this shows that the presented method is feasible and effective.关键词
径向基函数神经网络/风力发电场/年度发电量估计/风速Key words
radial basis function(RBF)neural network/wind power plant/annual energy output estimation/wind speed分类
信息技术与安全科学引用本文复制引用
成立,李茂军,许武,苏盛..RBF神经网络在风电场年度发电量估算中的应用[J].电力系统及其自动化学报,2016,28(11):32-36,5.基金项目
国家自然科学基金资助项目（50907005）；湖南高校创新平台开放基金资助项目 （）
