Visible to the public Motif-Based Analysis of Power Grid Robustness under Attacks

TitleMotif-Based Analysis of Power Grid Robustness under Attacks
Publication TypeConference Paper
Year of Publication2017
AuthorsDey, A. K., Gel, Y. R., Poor, H. V.
Conference Name2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
ISBN Number978-1-5090-5990-4
Keywordsbuilding blocks, complex network, complex networks, composability, compositionality, Europe, fragility analysis, global network topology, local dynamics, local network structure, local topological properties, Measurement, Metrics, Motif-based analysis, motifs, network motifs, node degree distribution, power grid system, power grid vulnerability analysis, power grids, power system classification, Power system dynamics, power system networks, Power systems, pubcrawl, resilience, Resiliency, Robustness, subgraphs, Tools, Topology
Abstract

Network motifs are often called the building blocks of networks. Analysis of motifs is found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. In the context of power grids, this phenomenon of the impact of local structure has been recently documented in fragility analysis and power system classification. At the same time, most studies of power system networks still tend to focus on global topo-logical measures of power grids, often failing to unveil hidden mechanisms behind vulnerability of real power systems and their dynamic response to malfunctions. In this paper a pilot study of motif-based analysis of power grid robustness under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of power systems.

URLhttps://ieeexplore.ieee.org/document/8309114/
DOI10.1109/GlobalSIP.2017.8309114
Citation Keydey_motif-based_2017