Abstract
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.
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Acknowledgements
This research has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 731232). The author would like to thank Sean McKenna and Bradley Eck, from IBM Research Ireland, for pointing to the data set used in this research, and Michele Berlingherio, from IBM Research Ireland, for providing excellent feedback on the manuscript.
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Fusco, F. (2018). Probabilistic Graphs for Sensor Data-Driven Modelling of Power Systems at Scale. In: Woon, W., Aung, Z., Catalina Feliú, A., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. Technologies, Systems and Society. DARE 2018. Lecture Notes in Computer Science(), vol 11325. Springer, Cham. https://doi.org/10.1007/978-3-030-04303-2_4
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