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

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. At the same time, these challenges are usually too complex to be solved with traditional approaches. In this gap, where traditional approaches are reaching their limits, Machine Learning has become a popular tool to bridge this shortcoming through data-driven approaches. To enable novel ML implementations is we propose FiN-2 dataset, the first large-scale real-world broadband powerline communications (PLC) dataset. FiN-2 was collected during real practical use in a part of the German low-voltage grid that supplies energy to over 4.4 million people and shows well over two billion data points 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 also 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.