摘要
应用独立分量分析算法,即基本FastICA算法,在不知道电力系统网络参数或拓扑结构的情况下,可以估计节点电力负荷曲线。若考虑测量中存在的噪声,则基本FastICA算法将含有噪声的观测信号进行分离,导致估计误差增大。为此,通过在白化数据过程中考虑噪声的作用,改进了FastICA算法。IEEE14母线系统的仿真实验结果表明,应用改进后FastICA算法所得到的负荷曲线估计结果的精度,要优于基本FastICA算法。说明改进后的FastICA算法适用于考虑噪声情况下的电力负荷曲线估计。
FastICA（Independent Component Analysis） algorithm is applied to estimate active load profiles without prior knowledge of the electric network model parameters or topology. However,estimation accuracy can be lowered considering the measurement noise because the conventional algorithm has to separate the observed data including the noise. Consequently,we improved the FastICA to meet the demands of more accurate results depending on the induction of noise during the process of whitening. The experimental results based on IEEE 14 system show that estimation accuracy of the improved FastICA is higher than that of the basic FastICA. It is concluded that the improved FastICA is appropriate for the load profiles estimation with noise.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2010年第4期1044-1049,共6页
High Voltage Engineering
基金
黑龙江省自然科学基金(E200932)
黑龙江省教育厅项目(11511075)
中国博士后基金(2005037656)
哈尔滨理工大学2009年青年拔尖人才计划~~
关键词
盲源分离
独立分量分析
解除管制
电力市场
负荷曲线估计
blind source separation
independent component analysis
deregulation
power market
load profile estimation
