Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids

TL;DR

This paper introduces two large-scale real-world datasets from power line communication infrastructure in German low-voltage grids, demonstrating their potential for advanced machine learning applications in grid management and maintenance.

Contribution

It presents the first large-scale real-world PLC datasets and explores their use in asset management, forecasting, and anomaly detection with novel machine learning methods.

Findings

Datasets contain over 13 billion data points from 5100 sensors.

Demonstrated applications include grid state visualization and predictive maintenance.

Highlighting the untapped potential of PLC data for smart grid management.

Abstract

Electricity grids have become an essential part of daily life, even if they are often not noticed in everyday life. We usually only become particularly aware of this dependence by the time the electricity grid is no longer available. However, significant changes, such as the transition to renewable energy (photovoltaic, wind turbines, etc.) and an increasing number of energy consumers with complex load profiles (electric vehicles, home battery systems, etc.), pose new challenges for the electricity grid. To address these challenges, we propose two first-of-its-kind datasets based on measurements in a broadband powerline communications (PLC) infrastructure. Both datasets FiN-1 and FiN-2, were collected during real practical use in a part of the German low-voltage grid that supplies around 4.4 million people and show more than 13 billion datapoints collected by more than 5100 sensors. In addition, we present different use cases in asset management, grid state visualization, forecasting, predictive maintenance, and novelty detection to highlight the benefits of these types of data. For these applications, we particularly highlight the use of novel machine learning architectures to extract rich information from real-world data that cannot be captured using traditional approaches. By publishing the first large-scale real-world dataset, we aim to shed light on the previously largely unrecognized potential of PLC data and emphasize machine-learning-based research in low-voltage distribution networks by presenting a variety of different use cases.