We consider distributed multi-area state estimation algorithms for power systems with switching communication graphs. The power system is partitioned into multiple geographically non-overlapping areas and each area is assigned with an estimator to give a local estimate of the entire power system’s state. The inter-area communication networks are assumed to switch among a finite set of digraphs. Each area runs a distributed estimation algorithm based on consensus+innovations strategies. By the binomial expansion of matrix products, time-varying system and algebraic graph theories, we prove that all area’s local estimates converge to the global least square estimate with probability 1 if the measurement system is jointly observable and the communication graphs are balanced and jointly strongly connected. Finally, we demonstrate the theoretical results by an IEEE 118-bus system.

中文翻译：

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具有切换通信图的电力系统分布式多区域状态估计

我们考虑具有切换通信图的电力系统的分布式多区域状态估计算法。电力系统被划分为多个地理上不重叠的区域，并且每个区域都分配有一个估计器，以对整个电力系统的状态进行本地估计。假设区域间通信网络在一组有限的有向图之间切换。每个区域都运行基于共识+创新策略的分布式估计算法。通过矩阵乘积的二项式展开，时变系统和代数图理论，我们证明了如果测量系统可以共同观测并且通信图是平衡且共同强的，则所有区域的局部估计都以概率1收敛到全局最小二乘估计。连接的。最后，