In this paper, a novel distributed dynamic state estimation (DSE) method for real-time monitoring of power systems is implemented. In modern large-scale power grids, the number of deployed meters and the frequency of collecting data have remarkably increased. Such a growth in the spatiotemporal size of collected data overwhelms the existing monitoring system with a centralized star structure. Streaming of data from all meters of the network to the central control center also increases communication latency. To overcome these challenges, the power system is partitioned into subsystems with local estimators, and the DSE process is distributed among local estimators. The distributed DSE is hosted at regional control centers and utilizes distributed extended Kalman filtering based on internodal transformation theory to estimate the dynamic states of power systems. The local estimators only require data within their own subsystem, and information is exchanged only between neighboring subsystems. The proposed distributed DSE is implemented on a 68-bus test system, and its accuracy is demonstrated.

中文翻译：

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电力系统的分布式动态估计


在本文中，实现了一种用于电力系统实时监控的新型分布式动态状态估计（DSE）方法。在现代大型电网中，部署的电表数量和采集数据的频率显着增加。所收集数据的时空规模的增长使现有的集中式星型结构的监控系统不堪重负。将数据从网络的所有仪表流式传输到中央控制中心也会增加通信延迟。为了克服这些挑战，电力系统被划分为具有局部估计器的子系统，并且 DSE 过程分布在局部估计器之间。分布式DSE托管在区域控制中心，利用基于节间变换理论的分布式扩展卡尔曼滤波来估计电力系统的动态状态。局部估计器只需要自己子系统内的数据，并且信息仅在相邻子系统之间交换。所提出的分布式DSE在68总线测试系统上实现，并证明了其准确性。