Analysis of cascading failure in power systems from a complex network perspective

In this thesis, a complex network perspective is taken to study the robustness of power systems against cascading failure. By abstracting generators, loads, and substations as nodes, and transmission lines as edges, a power system can be described by a network representation, through which the topological characteristics can be examined. The robustness of a power system is interpreted as its ability to resist cascading failure. In order to investigate the relationship between the network topology and the robustness performance, the key factor is to model the cascading failure processes appropriately. This thesis aims to study the cascading failure mechanism in power systems and to identify ways to enhance their robustness from a complex network perspective. First, we propose a circuit-based power flow model for the simulation of cascading failures and the robustness assessment of power systems. Based on Kirchhoff’s laws and the properties of network elements, and combined with a complex network structure, this model is able to assess the severity of a blackout. The blackout size is measured by the percentage of unserved nodes (PUN) caused by a failed component. For each component chosen as an initially failed component, a value of PUN can be found. Based on the PUN of each node, the percentage of non-critical links (PNL) is used to measure a power system’s robustness quantitatively. Simulation results on several real and synthesized networks show that connection having a short average shortest path length can jeopardize a power system’s robustness. Then, we model the dynamic propagation processes of cascading failures in power systems beginning from a dysfunctioned component and developing eventually to a v