On the Exploitation of Admittance Measurements for Wired Network Topology Derivation

TL;DR

This paper presents a novel method for deriving wired network topology using admittance measurements and transmission line theory, applicable even in noisy environments, with validation on power line network data.

Contribution

It introduces an analytic approach to infer network topology from admittance data and proposes a noise-robust algorithm validated on power line network scenarios.

Findings

Topology can be derived from admittance measurements under certain conditions.

The proposed algorithm performs well in noisy environments.

Validation shows effectiveness on power line distribution network data.

Abstract

The knowledge of the topology of a wired network is often of fundamental importance. For instance, in the context of Power Line Communications (PLC) networks it is helpful to implement data routing strategies, while in power distribution networks and Smart Micro Grids (SMG) it is required for grid monitoring and for power flow management. In this paper, we use the transmission line theory to shed new light and to show how the topological properties of a wired network can be found exploiting admittance measurements at the nodes. An analytic proof is reported to show that the derivation of the topology can be done in complex networks under certain assumptions. We also analyze the effect of the network background noise on admittance measurements. In this respect, we propose a topology derivation algorithm that works in the presence of noise. We finally analyze the performance of the algorithm using values that are typical of power line distribution networks.