Leverage Point Identification Method for LAV-Based State Estimation

TL;DR

This paper introduces a new method for identifying leverage points in LAV-based state estimation, improving robustness against gross measurement errors and outperforming traditional approaches like Projection Statistics.

Contribution

It presents a novel lemma for leverage point identifiability and an algorithm validated through extensive simulations and a power system application.

Findings

Proposed method outperforms Projection Statistics in leverage point detection.

The algorithm accurately identifies all leverage points regardless of measurement errors.

Application to power system state estimation demonstrates practical effectiveness.

Abstract

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.