可再生能源2018，Vol.36Issue(3)：438-445,8.
基于实测数据的风电场稳态等值建模研究
Research on wind farm steady-state equivalent modeling based on measured data
摘要
Abstract
In order to resolve complexity and large calculation of large-scale wind farm simulation model,a new method of wind farm steady-state equivalent modeling with measured data is proposed in this paper. Utilizing the particle filter algorithm,various interferences in measured wind speed can be eliminated;in consideration of wind regime differences between wind turbines,the K-means clustering algorithm is introduced to extract the featured wind speed reflecting wind regime differences so as to simplify the modeling process;the featured wind speed and measured power data are collected respectively as the input and output signals,BP neural network is employed to establish the wind farm steady-state equivalent model.The proposed model involves topography and turbines distribution of the actual wind farm,measured data are adopted to analyze its generalization ability and verify its accuracy,simulation results indicates that the modeling method is precise and rational.关键词
风电场/实测数据/BP神经网络/稳态/等值建模Key words
wind farm/measured data/BP neural network/steady-state/equivalent modeling分类
能源科技引用本文复制引用
李牡丹,王印松,刘霜..基于实测数据的风电场稳态等值建模研究[J].可再生能源,2018,36(3):438-445,8.基金项目
华北电力大学中央高校基本科研业务费专项资金资助项目(9161715008). （9161715008）
