论文标题
基于LAV的状态估计的杠杆点标识方法
Leverage Point Identification Method for LAV-Based State Estimation
论文作者
论文摘要
状态估计问题可以通过不同的方法解决。就鲁棒性而言，一种有效的方法由最小绝对价值（LAV）估算器表示，尽管容易受到利用点的影响。根据先前提出的定理，在本文中，我们阐述并严格证明了一种新的引理，证明了基于LAV的状态估计中杠杆点的可识别性。在这些理论基础的基础上，我们提出了一种用于杠杆点识别的算法，该算法通过广泛的数值模拟验证，并与更传统的方法进行了比较，例如投影统计（PS）。获得的结果证实，所提出的方法的表现优于PS，代表了基于LAV的状态估计器的显着增强，因为它正确识别了所有杠杆点，而与测量总误差的准确性或存在无关。最终包括并讨论了有关电力系统状态估计的专用应用程序示例。
The state estimation problem can be solved through different methods. In terms of robustness, an effective approach is represented by the Least Absolute Value (LAV) estimator, though vulnerable to leverage points. Based on a previously proposed theorem, in this paper we enunciate, and rigorously demonstrate, a new lemma that proves the identifiability of leverage points in LAV-based state estimation. On the basis of these theoretical foundations, we propose an algorithm for leverage point identification whose performance is validated by means of extensive numerical simulations and compared against more traditional approaches, like Projection Statistics (PS). The obtained results confirm that the proposed method outperforms PS and represents a significant enhancement for LAV-based state estimators as it correctly identifies all the leverage points, independently of the accuracy or the presence of measurement gross errors. A dedicated application example with respect to power system state estimation is finally included and discussed.
