Power Line Communication and Sensing Using Time Series Forecasting

TL;DR

This paper presents a novel power line sensing method that uses time-series forecasting and statistical testing to detect cable anomalies without prior knowledge, enhancing smart grid diagnostics.

Contribution

It introduces a universal, data-driven power line sensing technique based on forecasting and statistical analysis, eliminating the need for prior domain knowledge.

Findings

Effective detection of cable faults demonstrated on synthetic data.

Validated approach with real-world data from distribution networks.

Universal applicability across different network types.

Abstract

Smart electrical grids rely on data communication to support their operation and on sensing for diagnostics and maintenance. Usually, the roles of communication and sensing equipment are different, i.e., communication equipment does not participate in sensing tasks and vice versa. Power line communication (PLC) offers a cost-effective solution for joint communication and sensing for smart grids. This is because the high-frequency PLC signals used for data communication also reveal detailed information regarding the health of the power lines that they travel through. Traditional PLC-based power line or cable diagnostic solutions are dependent on prior knowledge of the cable type, network topology, and/or characteristics of the anomalies. In this paper, we develop a power line sensing technique that can detect various types of cable anomalies without any prior domain knowledge. To this end, we design a solution that first uses time-series forecasting to predict the PLC channel state information at any given point in time based on its historical data. Under the approximation that the prediction error follows a Gaussian distribution, we then perform chi-squared statistical test to build an anomaly detector which identifies the occurrence of a cable fault. We demonstrate the effectiveness and universality of our sensing solution via evaluations conducted using both synthetic and real-world data extracted from low- and medium-voltage distribution networks.