Cable Diagnostics with Power Line Modems for Smart Grid Monitoring

TL;DR

This paper introduces a machine learning-based framework utilizing existing power line modems as sensors for continuous, automatic cable health diagnostics in smart grids, combining reflectometry features with simulation validation.

Contribution

It proposes a novel ML-based cable diagnostic method using PLMs as sensors, integrating reflectometry features, and demonstrating effectiveness through simulations in smart grid scenarios.

Findings

Effective detection of cable degradations under various aging conditions

Robustness of the diagnostic method against different load configurations

Comparison shows advantages over existing diagnostic approaches

Abstract

Remote monitoring of electrical cable conditions is an essential characteristic of the next-generation smart grid, which features the ability to consistently surveil and control the grid infrastructure. In this paper, we propose a technique that harnesses power line modems (PLMs) as sensors for monitoring cable health. We envisage that all or most of these PLMs have already been deployed for data communication purposes and focus on the distribution grid or neighborhood area networks in the smart grid. For such a setting, we propose a machine learning (ML) based framework for automatic cable diagnostics by continuously monitoring the cable status to identify, assess, and locate possible degradations. As part of our technique, we also synthesize state-of-the-art reflectometry methods within the PLMs to extract beneficial features for effective performance of our proposed ML solution. Simulation results demonstrate the effectiveness of our solution under different aging conditions and varying load configurations. Finally, we reflect on our proposed diagnostics method by evaluating its robustness and comparing it with existing alternatives.