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

工程研究启动补助金：大型电力网络的高效分层状态估计器（REU 补充）

• 批准号: 8909412
• 负责人: Abdurrahim El-Keib
• 金额: $ 5.27万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-06-01 至 1992-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8909412&HistoricalAwards=false
• 关键词: Engineering; Research; Initiation; Grant; Efficient

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.

本项目的目标是研究一种有效的静态 大型电力系统状态估计器 的估计器 包括执行两级计算：在较低的（第一） 在分级级别，执行常规状态估计 同时对所有子系统;然后这些局部估计是 在上级协调。 该方法利用物理 电力系统的特性，并制定了当地的状态估计 问题作为两个线性规划子问题， 通过所提出的方法依次求解。 这也表明 研究Dantzig分解原理的一种形式， 减少狼的大小问题。 标准IEEE和其他 现有的电力系统将用于测试和比较 目的 拟议的研究提出了创新技术， 解决了大规模电力系统的状态估计问题。 它肯定会增加对性质的理解， 线性规划状态估计，并建议补救措施， 他们可能遇到的问题。 预计还将导致 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。