Underdetermined Library-aided Impedance Estimation with Terminal Smart Meter Data

TL;DR

This paper introduces a novel method for impedance estimation using smart meter data that handles ambiguity and unobserved network nodes, leveraging prior information and supporting topology identification with high accuracy.

Contribution

It proposes a unifying framework for data- and structure-driven impedance identification, accommodating ambiguous topologies and providing a collection of compatible solutions.

Findings

High identification accuracy on benchmark datasets

Method supports topology identification without degree guarantees

Robust to noisy input data

Abstract

Smart meters provide relevant information for impedance identification, but they lack global phase alignment and internal network nodes are often unobserved. A few methods for this setting were developed, but they have requirements on data correlation and/or network topology. In this paper, we offer a unifying view of data- and structure-driven identifiability issues, and use this groundwork to propose a method for underdetermined impedance identification. The method can handle intrinsically ambiguous topologies and data; its output is not forcedly a single estimate, but instead a collection of data-compatible impedance assignments. It uses a library of plausible commercial cable types as a prior to refine the solutions, and we show how it can support topology identification workflows built around known georeferenced joints without degree guarantees. The method depends on a small number of non-sensitive parameters and achieves high identification performance on a sizeable benchmark case even with low-size injection/voltage datasets. We identify key steps that can be accelerated via GPU-based parallelization. Finally, we assess the tolerance of the identification to noisy input.