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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References
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2016)
Abur, A., Exposito, A.G.: Detecting multiple solutions in state estimation in the presence of current magnitude measurements. IEEE Trans. Power Syst. 12(1), 370–375 (1997). https://doi.org/10.1109/59.575721
Abur, A., Exposito, G.A.: Power System State Estimation: Theory and Implementation. CRC Press, Boca Raton (2004)
Barber, D.: Bayesian Reasoning and Machine Learning. Cambridge University Press, Cambridge (2012)
Collier, S.E.: The emerging enernet: convergence of the smart grid with the Internet of Things. IEEE Ind. Appl. Mag. 23(2) (2017)
Cosovic, M., Vukobratovic, D.: Distributed Gauss-Newton method for AC state estimation: a belief propagation approach. In: IEEE International Conference on Smart Grid Communications (SmartGridComm) (2016)
Cosovic, M., Vukobratovic, D.: State estimation in electric power systems using belief propagation: an extended DC model. In: IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2016)
Fiedler, M.: Laplacian of graphs and algebraic connectivity” and combinatorics and graph theory. 25(1), 57–70. Banach Center Publications (1989)
Frey, B.J., Jojic, N.: A comparison of algorithms for inference and learning in probabilistic graphical models. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1392–1416 (2005)
Fusco, F., Thirupathi, S., Gormally, R.: Power systems data fusion based on belief propagation. In: Proceedings of the IEEE PES Innovative Smart Grid Technologies (ISGT) Conference Europe (2017)
Honkela, A., Valpola, H.: Unsupervised variational Bayesian learning of nonlinear models. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS) 17 (2004)
Hu, Y., Kuh, A., Kavcic, A., Yang, T.: A belief propagation based power distribution system state estimator. IEEE Comput. Intell. Mag. 6, 36–46 (2011)
Jensen, T.V., Pinson, P.: Re-Europe and a large-scale dataset for modeling a highly renewable european electricity system. Sci. Data 4, 170–175 (2017)
Josz, C., Fliscounakis, S., Maeght, J., Panciatici, P.: AC power flow data in MATPOWER and QCQP format: iTesla and RTE snapshots and and PEGASE. arXiv:1603.01533 (2016)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: Proceedings of the Sixth International Conference on Learning Representations (ICLR) (2014)
Koller, D., Friedman, N.: Probabilistic Graphical Models. MIT Press, Cambridge (2009)
Luttinen, J., Llin, A.: Variational Gaussian-process factor analysis for modeling spatio-temporal data. In: Advances in Neural Information Processing Systems (NIPS) 22 (2009)
Nguyen, M.H., De la Torre, F.: Robust kernel principal component analysis. In: Advances in Neural Information Processing Systems (NIPS) 22 (2009)
Sanguinetti, G., Lawrence, N.D.: Missing data in Kernel PCA. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 751–758. Springer, Heidelberg (2006). https://doi.org/10.1007/11871842_76
Scholz, M., Kaplan, F., Guy, C.L., Kopka, J., Selbig, J.: Gene expression non-linear PCA : a missing data approach. Bioinformatics 21(20), 3887–3895 (2005). https://doi.org/10.1093/bioinformatics/bti634
Zimmerman, R.D., Murillo-Sánchez, C.E., Thomas, R.J.: Matpower: steady-state operations and planning and analysis tools for power systems research and education. IEEE Trans. Power Syst. 26(1), 12–19 (2011)
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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