A Kalman-Based Coordination for Hierarchical State Estimation: Agorithm and Analysis

Hierarchical state estimation algorithms are usually employed in large-scale interconnected power systems, where state estimation usually involves very tedious communications and computations. This paper presents 1) a modified coordination technique that is based on Kalman filtering, derived from hierarchical state estimation; and 2) a time complexity analysis and experimental implementation to compare central, distributed, and hierarchical state estimation algorithms in terms of computation power and communication bandwidth requirements. Analytical and experimental results on the IEEE 118-bus test bed show that the presented approach, i.e., hierarchical Kalman filtering (HKF), needs about 34% communication bandwidth and O(1/N³) computation power in subsystems compared to central state estimation, while giving approximately the same level of estimation precision.