抗差估计法应用于状态估计中不良数据的检测和辨识Huber Estimation Applied to Detect and Identify Bad Data in State Eestimation
刘兰,黄彦全,李云飞,绍明
摘要(Abstract)：
电力系统状态估计是电能管理系统(EMS)的重要组成部分,然而在实际运行的电网自动化系统中,量测数据中不可避免含有不良数据,从而影响状态估计的结果。不良数据的检测是电力系统状态估计的重要功能之一,它能够排除量测采样数据中偶然出现的少数不良数据,提高状态估计的可靠性。文章采用突变检测和抗差估计相结合的方法,并在IEEE118节点网络上进行了验证,结果证明该方法检测辨识的有效性和可靠性。
The state estimation is an important part of EMS in power systems. But the existence of bad data in measurement data in Electrical Network Automation can't be avoided, and they will influence the estimation results. Bad data detection is an important function of state estimation in power systems, it can eliminate little bad data while appear in measure sampling data by chance, for enhancing the reliability of state estimation. The paper uses a way while is combined by saltation detection and huber estimation, and validates it on power systems of IEEE118. The result shows availability and reliability of the way in detection and identification.
关键词(KeyWords)：
抗差估计;不良数据;突变;检测;辨识
huber estimation ; bad data ; saltation ; detection ; identification.
基金项目(Foundation):
作者(Author):
刘兰,黄彦全,李云飞,绍明
DOI: 10.19585/j.zjdl.2006.05.002
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