Comparative Analysis of Information Theoretic and Statistical Methods for Line Parameter Estimation

TL;DR

This paper compares four advanced methods for line parameter estimation using PMU data, emphasizing the impact of non-Gaussian noise on their performance and identifying conditions for optimal results.

Contribution

It provides a comprehensive comparison of information theoretic and statistical methods for LPE under non-Gaussian noise, highlighting their strengths and limitations.

Findings

Performance varies significantly with noise distribution

Certain methods outperform others under specific noise conditions

Guidelines for selecting suitable estimation techniques

Abstract

Recent studies indicate that the noise characteristics of phasor measurement units (PMUs) can be more accurately described by non-Gaussian distributions. Consequently, estimation techniques based on Gaussian noise assumptions may produce poor results with PMU data. This paper considers the PMU based line parameter estimation (LPE) problem, and investigates the performance of four state-of-the-art techniques in solving this problem in presence of non-Gaussian measurement noise. The rigorous comparative analysis highlights the merits and demerits of each technique w.r.t. the LPE problem, and identifies conditions under which they are expected to give good results.