Topology-Based Machine Learning: Predicting Power Line Communication Quality in Smart Grids

Abstract

Smart Grids (SG) envision the exchange of both power and data, enabling system and customers to generate and transfer energy in a more efficient and balanced way. Among the relevant communication technologies, we find Power-Line Communications (PLC), which allow for data transmission on the electrical cables used for power delivery. Despite the hostile medium, PLC offer reliability and data rates to support exchange of control traffic, smart metering and polling applications. The distribution portion of the power delivery network, which we focus on in this work, is the most topologically complex, which makes channel prediction complicated. We show how random realistic topologies can be generated and then used to train a Machine Learning (ML) algorithm to infer PLC link quality (based on channel response) based solely on topology descriptors. We eventually show how precisely the communication quality can be inferred from the SG topology through ML. In doing so, we also discuss how the ML approach offers the common ground between top-down and bottom-up approaches for network characterization and how it enables smart decision making in the SG.

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

“…Moreover, the effectiveness of PLC network devices, commonly employed in these studies, has been enhanced through the deterministic data optimization approach [149]. Additionally, the suitability of PLC impedance estimation has been discussed [150,151]. The performance of estimators was thoroughly analyzed and evaluated through numerical assessments [152].…”