Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale
This addresses the need for more adaptable modeling tools in power grid planning and operation, though it appears incremental as it builds on existing probabilistic graph methods.
The paper tackles the challenge of modeling complex power systems by proposing a data-driven approach using probabilistic graphs, which achieved accuracy and scalability on a large-scale European transmission grid dataset.
The growing complexity of the power grid, driven by increasing share of distributed energy resources and by massive deployment of intelligent internet-connected devices, requires new modelling tools for planning and operation. Physics-based state estimation models currently used for data filtering, prediction and anomaly detection are hard to maintain and adapt to the ever-changing complex dynamics of the power system. A data-driven approach based on probabilistic graphs is proposed, where custom non-linear, localised models of the joint density of subset of system variables can be combined to model arbitrarily large and complex systems. The graphical model allows to naturally embed domain knowledge in the form of variables dependency structure or local quantitative relationships. A specific instance where neural-network models are used to represent the local joint densities is proposed, although the methodology generalises to other model classes. Accuracy and scalability are evaluated on a large-scale data set representative of the European transmission grid.