DocumentCode :
656708
Title :
Blind topology identification for power systems
Author :
Xiao Li ; Poor, H. Vincent ; Scaglione, Anna
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of California, Davis, Davis, CA, USA
fYear :
2013
fDate :
21-24 Oct. 2013
Firstpage :
91
Lastpage :
96
Abstract :
In this paper, the blind topology identification problem for power systems only using power injection data at each bus is considered. As metering becomes widespread in the smart grid, a natural question arising is how much information about the underlying infrastructure can be inferred from such data. The identifiability of the grid topology is studied, and an efficient learning algorithm to estimate the grid Laplacian matrix (i.e., the graph equivalent of the grid admittance matrix) is proposed. Finally, the performance of our algorithm for the IEEE-14 bus system is demonstrated, and the consistency of the recovered graph with the true graph associated with the underlying power grid is shown in simulations.
Keywords :
IEEE standards; graph theory; learning (artificial intelligence); matrix algebra; power system identification; power system measurement; power system simulation; smart power grids; IEEE-14 bus system; blind topology identification problem; grid Laplacian matrix estimation; grid admittance matrix; grid topology identification; learning algorithm; power grid simulation; power injection data; power system; smart grid metering; true graph recovery; Eigenvalues and eigenfunctions; Laplace equations; Load modeling; Monitoring; Null space; Power grids; Topology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
Type :
conf
DOI :
10.1109/SmartGridComm.2013.6687939
Filename :
6687939
Link To Document :
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