Identification of multiple interacting bad data via power system decomposition
Identification of multiple interacting bad data via power system decomposition
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DOI：
10.1109/59.535697
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发表时间：
1996-08
影响因子：
6.6
通讯作者：
M. G. Cheniae;L. Mili;P. Rousseeuw
中科院分区：
文献类型：
--
作者：
M. G. Cheniae;L. Mili;P. Rousseeuw
针对电力系统状态估计问题，提出了一种新的、强鲁棒性的不良数据辨识算法。系统分解方案与最小平方中位数估计器相结合，即使在一致性误差的情况下也允许识别多个相互作用的坏数据。该算法对杠杆位置的不良测量具有很强的抵抗能力，没有先验的测量误差概率分布假设，适用于实时环境。
This paper presents a new, highly robust bad data identification algorithm for electric power system state estimation. A system decomposition scheme is coupled with the least median of squares estimator to allow identification of multiple interacting bad data even in cases of conforming errors. The algorithm is inherently resistant to bad measurements in positions of leverage, makes no a priori measurement error probability distribution assumptions, and is applicable in a real-time environment.