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Dynamic State Estimation for Power Networks by Distributed Unscented Information Filter

机译：分布式无编码信息滤波器动态状态估计电源网络

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摘要

This paper presents a distributed unscented information filtering (UIF) method for state estimation of interconnected nonlinear dynamic systems. The UIF method is an information filter based on unscented transformation (UT), which is a nonlinear estimation method. Therefore, the estimation accuracy of UT-based distributed UIF method should be higher than that of linearization-based distributed maximum a posteriori (MAP) method. When implementing the distributed UIF algorithm, we first calculate the local estimate by UIF method based on the local observations, and then gradually integrate the neighboring information by iterative method to obtain a more accurate distributed estimate. The IEEE 118-bus system is adopted to conduct a series of simulations to evaluate the performance of the proposed distributed UIF method, the centralized UIF estimator, local UIF estimator and the distributed MAP estimator. Simulation results show that the proposed distributed UIF approach achieves a worse estimation accuracy than the centralized UIF, but is better than both the distributed MAP estimator and the local UIF estimator.
机译：本文介绍了用于互连非线性动态系统的状态估计的分布式无容信息滤波（UIF）方法。 UIF方法是基于未加换的变换（UT）的信息滤波器，其是非线性估计方法。因此，基于UT的分布式UIF方法的估计精度应高于基于线性化的分布式最大值后后验（MAP）方法的估计精度。当实现分布式UIF算法时，我们首先通过基于本地观察来计算UIF方法的本地估计，然后通过迭代方法逐渐集成相邻信息以获得更准确的分布式估计。采用IEEE 118总线系统进行一系列模拟，以评估所提出的分布式UIF方法，集中式UIF估计器，本地UIF估计器和分布式地图估计器的性能。仿真结果表明，所提出的分布式UIF方法实现比集中式UIF更差的估计精度，但优于分布式地图估计器和本地UIF估计器。

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