Power Line Communication Based Smart Grid Asset Monitoring Using Time Series Forecasting

Monitoring grid assets continuously is critical in ensuring the reliable operation of the electricity grid system and improving its resilience in case of a defect. In light of several asset monitoring techniques in use, power line communication (PLC) enables a low-cost cable diagnostics solution by re-using smart grid data communication modems to also infer the cable health using the inherently estimated communication channel state information. Traditional PLC-based cable diagnostics solutions are dependent on prior knowledge of the cable type, network topology, and/or characteristics of the anomalies. In contrast, we develop an asset monitoring technique in this paper that can detect various types of anomalies in the grid 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 assumption that the prediction error follows a Gaussian distribution, we then perform chi-squared statistical test to determine the significance level of the resultant Mahalanobis distance to build our anomaly detector. We demonstrate the effectiveness and universality of our solution via evaluations conducted using both synthetic and real-world data extracted from low- and medium-voltage distribution networks.