Visible to the public Analysis of cascaded failures in power networks using maximum flow based complex network approach

TitleAnalysis of cascaded failures in power networks using maximum flow based complex network approach
Publication TypeConference Paper
Year of Publication2016
AuthorsGhanbari, R., Jalili, M., Yu, X.
Conference NameIECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society
KeywordsAdmittance, cascaded failure, cascaded failure analysis, complex network, complex networks, composability, failure analysis, Generators, IEEE 118 bus network, largest connected component, Load modeling, Mathematical model, maximum flow, Metrics, power grid vulnerability analysis, power grids, power network, power system reliability, power system security, power transmission lines, pubcrawl, random spatial networks, Resiliency, Vulnerability

Power networks can be modeled as networked structures with nodes representing the bus bars (connected to generator, loads and transformers) and links representing the transmission lines. In this manuscript we study cascaded failures in power networks. As network structures we consider IEEE 118 bus network and a random spatial model network with similar properties to IEEE 118 bus network. A maximum flow based model is used to find the central edges. We study cascaded failures triggered by both random and targeted attacks to the edges. In the targeted attack the edge with the maximum centrality value is disconnected from the network. A number of metrics including the size of the largest connected component, the number of failed edges, the average maximum flow and the global efficiency are studied as a function of capacity parameter (edge critical load is proportional to its capacity parameter and nominal centrality value). For each case we identify the critical capacity parameter by which the network shows resilient behavior against failures. The experiments show that one should further protect the network for a targeted attack as compared to a random failure.

Citation Keyghanbari_analysis_2016