Chen Tengpeng, Foo Yi Shyh Eddy, Ling K V, Chen Xuebing
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel). 2017 Oct 11;17(10):2310. doi: 10.3390/s17102310.
In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load.
本文针对大规模电力系统状态估计问题，提出了一种基于滚动时域估计（MHE）的分布式状态估计方法。该方法将电力系统划分为若干个状态不重叠的局部区域。与将所有测量值发送到一个处理中心的集中式方法不同，该方法将状态估计任务分配到收集本地测量值的本地处理中心。受分区滚动时域估计（PMHE）算法的启发，每个局部区域通过使用本地测量值和来自相邻区域的估计结果来求解一个较小的优化问题，以估计其自身的局部状态。与PMHE相比，我们的方法忽略了过程模型的误差。所提出的改进型PMHE（mPMHE）方法在优化过程中还可以考虑状态约束，从而进一步减轻异常值的影响。在IEEE 14节点和118节点系统上的仿真结果验证了我们的方法在实现相当的状态估计精度的同时，整体计算负荷显著降低。
