Engineering Research Initiation Grant: An Efficient Hierarchical State Estimator for Large Power Networks (REU Supplement)

本项目的目标是研究一种适用于超大规模电力系统的高效静态估计器。所提出的估值器包括执行两级计算：在较低(第一)分级级别，对所有子系统同时执行常规状态估计；然后在较高级别协调这些局部估计。该方法利用电力系统的物理特性，将局部状态估计问题转化为两个线性规划子问题，然后用所提出的方法依次求解。它还建议研究Dantzig和Wolf分解原理的一种形式，以减少问题的规模。标准的IEEE和其他现有的电力系统将用于测试和比较目的。该研究提出了解决大规模电力系统状态估计问题的创新技术。这肯定会增加对线性规划状态估值器性质的理解，并将为它们可能存在的问题提供补救措施。这也有望产生一种新的分解状态估计问题的方法，适用于线性规划状态估计器的应用。

The objective of this project is to investigate an efficient static state estimator for very large power systems. The proposed estimator consists of performing a two-level calculation: in the lower (first) hierarchical level, a conventional state estimation is performed simultaneously for all subsystems; these local estimations are then coordinated at the upper level. The approach exploites the physical properties of power systems and formulates the local state estimation problem as two linear programming sub-problems which would be sequentially solved by a proposed method. It also suggests investigating a form of the decomposition principle of Dantzig and Wolf to reduce the size of the problem. Standard IEEE and other existing power systems will be used for testing and comparison purposes. The proposed research suggests innovative techniques to solve the problem of state estimation on large scale power systems. It will certainly increase the understanding of the properties of linear programming state estimators and would suggest remedies to the problems which they might have. It is also expected to result in a new approach for decomposing the state estimation problem that is suitable for application with linear programming state estimators.