DocumentCode :
3578260
Title :
Research on Efficient Detection Methods for False Data Injection in Smart Grid
Author :
Yun Liu ; Lei Yan ; Jian-Wei Ren ; Dan Su
Author_Institution :
State Grid Jibei Electr. Power Co. Ltd., Beijing, China
fYear :
2014
Firstpage :
188
Lastpage :
192
Abstract :
Smart grid is built by the combination of information technology and electric technology based on traditional power grid, and achieves the interaction between power generation and power utilization. However, new security issues appears together with the new cyber-physical power system, and false data injection is one of the attack means, which is impressive for it´s difficult from being detected. In this paper, the principle of false data injection is stated, and proposed two distributed detect algorithms. One is DOID (Distributed Observable Island Detection algorithm), which is based on observable islands theory. If a line connected by two nodes is pass through different observable islands, false data injection detected in this line. The other algorithm is DTAD (Distributed Time Approaching Detection algorithm). For one measuring point, if the state estimation values vary a lot, then attack detected. The simulation shows our methods can detect false data injection efficiently with low cost.
Keywords :
distributed power generation; smart power grids; DOID; DTAD; cyber-physical power system; distributed observable island detection algorithm; distributed time approaching detection algorithm; efficient detection methods; electric technology; false data injection; information technology; observable islands theory; power grid; power utilization; smart grid; Biological system modeling; Computational modeling; Data models; Smart grids; State estimation; Vectors; attack detection; false data injection; smart grid;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communication and Sensor Network (WCSN), 2014 International Conference on
Print_ISBN :
978-1-4799-7090-2
Type :
conf
DOI :
10.1109/WCSN.2014.45
Filename :
7061721
Link To Document :
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