标题： 有界不确定性的线性状态估计：比较分析 显示英文标题

标题： Linear State Estimation in Presence of Bounded Uncertainties: A Comparative Analysis

摘要： 多种算法已被提出以解决在数据存在不确定性情况下的电力系统状态估计问题。 然而，对处理模型扰动的关注较少。 在本论文所关注的线性状态估计（LSE）背景下，模型扰动来源于线路参数的变化。 由于实际线路参数值可能与电力公司数据库中的值不同，本文研究了三种方法，在数据和模型存在有界不确定性的情况下估计状态。 第一种方法基于区间算术，第二种方法基于凸优化，第三种方法基于广义线性分数规划。 这三种算法被应用于多个IEEE测试系统，并根据其速度和准确性进行比较。 结果表明，前两种算法非常快速且结果合理，而第三种方法在可扩展性方面存在问题，不适用于LSE。 显示英文摘要

摘要： A variety of algorithms have been proposed to address the power system state estimation problem in the presence of uncertainties in the data. However, less emphasis has been given to handling perturbations in the model. In the context of linear state estimation (LSE), which is the focus of this paper, perturbations in the model come from variations in the line parameters. Since the actual values of the line parameters can be different from the values stored in a power utility's database, we investigate three approaches in this paper to estimate the states in the presence of bounded uncertainties in the data and the model. The first approach is based on interval arithmetic, the second is based on convex optimization, and the third is based on generalized linear fractional programming. The three algorithms are applied to multiple IEEE test systems and compared in terms of their speed and accuracy. The results indicate that the first two algorithms are extremely fast and give expected results, while the third suffers from scalability issues and is unsuitable for LSE.