Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems
Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems
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DOI：
10.1016/j.ijepes.2014.09.024
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发表时间：
2015-02
影响因子：
5.2
通讯作者：
Xiangyun Qing;H. Karimi;Y. Niu;Xingyu Wang
中科院分区：
文献类型：
--
作者：
Xiangyun Qing;H. Karimi;Y. Niu;Xingyu Wang
提出了一种基于一致性算法的分散无迹卡尔曼滤波(UKF)多区域电力系统动态估计方法。整个系统被分成一定数量的非重叠区域。首先，每个区域使用UKF进行基于局部测量的动态状态估计。下一步，一致性算法只需要在相邻区域之间执行本地通信，以传播本地状态信息。最后，根据一致性算法得到的全局状态信息，对每个区域再次运行UKF。在IEEE 14节点和118节点系统上与无共识的分布式UKF算法进行了性能比较。分散UKF的低通信要求和高估计精度使其成为多区域电力系统动态状态估计的一种替代方案。
A decentralized unscented Kalman filter (UKF) method based on a consensus algorithm for multi-area power system dynamic state estimation is presented in this paper. The overall system is split into a certain number of non-overlapping areas. Firstly, each area executes its own dynamic state estimation based on local measurements by using the UKF. Next, the consensus algorithm is required to perform only local communications between neighboring areas to diffuse local state information. Finally, according to the global state information obtained by the consensus algorithm, the UKF is run again for each area. Its performance is compared with the distributed UKF without consensus algorithm on the IEEE 14-bus and 118-bus systems. The low communication requirements and high estimation accuracy of the decentralized UKF make it an alternative solution to the multi-area power system dynamic state estimation.