新息图拓扑可观测性及不良数据可辨识性分析

新息图状态估计在拓扑结构获取和不良数据辨识方面与一般状态估计差异较大。文中研究了新息图状态估计拓扑结构可观测性、量测数据的相关性、不良数据的可检测性和可辨识性问题。由获得连支推算新息的必要条件确定拓扑结构可观测性;利用新息差向量的表现特征分析不良数据可检测性和可辨识性,最后给出了较优的辨识连支不良数据的顺序。对该算法的分析及IEEE30节点系统算例表明,新息图状态估计中不良数据可辨识性可进行定性分析,其辨识不正常事件的能力强,量测冗余度要求较低。

Topology Observability and Bad Data Identifiability Analysis of the Innovation Graph Technique

In terms of state estimation, there are great differences between the innovation graph technique and the conventional methods on obtaining the topology configuration and identifying bad data. This paper is concerned with the observability of topology configuration, the correlation of measurement data, the detectability and identifiability of the bad data in the innovation graph technique. In the technique, the observability is determined by the requirement of obtaining the reckoned innovation vector. The detectability and identifiability can be analyzed by the feature of the innovation difference vector. An optimized sequence for identifying the related bad data is proposed. An analysis on this technique and the test on IEEE 30-bus system show that the bad data identifiability can be analyzed qualitatively. The innovation graph technique is robust in identifying anomalies owing to its low requirement on measurement redundancy.