Cascading Failure Analysis of Cyber Physical Power System With Multiple Interdependency and Control Threshold
Author(s) -
Yu Chen,
Yong Li,
Wenguo Li,
Xiaorui Wu,
Ye Cai,
Yijia Cao,
Christian Rehtanz
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2855441
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The traditional infrastructure in power system is undergoing a transition to the Smart Grid, in which the communication network and power grid will be integrated into a cyber-physical power system (CPPS). Although the traditional topological analysis reveals the mechanism of cascading failure between two networks, it ignores the control redundancy and standby lines from communication network to power grid. The robustness analysis in CPPS requires a more comprehensive model to analyze failure behavior in reality. Here, we propose a cascading failure model with one-to-multiple interdependency and a relevant theoretical framework to analyze CPPS cascading failure. In consideration of real CPPS, in the proposed model we introduce two robustness factors, the number of dependent links and control threshold, which can better describe the control function from communication nodes to power nodes. The remaining fraction under different initial attacking on high voltage transmission network, small world network, double star network, and the different topological combination of CPPS are analyzed. The results show that the proposed model and robustness factors can better reveal the robustness and the mechanism of two networks in cascading failure.
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