Power Systems Data Fusion based on Belief Propagation

Autor:	Fusco, Francesco, Tirupathi, Seshu, Gormally, Robert
Rok vydání:	2017
Předmět:	Statistics - Machine Learning Computer Science - Systems and Control Statistics - Applications
Druh dokumentu:	Working Paper
Popis:	The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices re- quires novel tools for providing a unified and consistent view of the system. A computational framework for power systems data fusion, based on probabilistic graphical models, capable of combining heterogeneous data sources with classical state estimation nodes and other customised computational nodes, is proposed. The framework allows flexible extension of the notion of grid state beyond the view of flows and injection in bus-branch models, and an efficient, naturally distributed inference algorithm can be derived. An application of the data fusion model to the quantification of distributed solar energy is proposed through numerical examples based on semi-synthetic simulations of the standard IEEE 14-bus test case. Comment: Version as accepted for publication at the 7th IEEE International Conference on Innovative Smart Grid Technologies (ISGT) Europe 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1705.08815 Zobrazit plný text záznamu View this record from Arxiv