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|Title:||Analysis of cascading failure in power systems from a complex network perspective||Authors:||Zhang, Xi||Advisors:||Tse, Michael (EIE)||Keywords:||Electric power systems -- Control
|Issue Date:||2017||Publisher:||The Hong Kong Polytechnic University||Abstract:||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 fow 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 large-scale blackout. Observing that in several historical power blackout events, the failure propagation profles share a common pattern characterized by a relatively slow initial phase followed by a sharp escalation of failure events, we further develop a method for fnding the time instants of failure events to complete the cascading failure modeling. The proposed circuit-based power fow model is adopted to derive the overloading conditions, which determine the failure rates of the elements. A stochastic method is then used to generate the uncertain failure time instants. The use of stochastic method addresses the uncertainties in individual components' physical failure mechanisms. Simulation results for the UIUC 150 Bus system show that the dynamic cascading failure profles generated by this model contain the typical features displayed in historical blackout data. Finally, we present a study of cascading failure in power systems that are coupled with cyber networks. In reality, the power network is linked to the cyber network for control purposes, and the cyber network is powered by the power network. The failure in one network can propagate to the other, and vice versa. Thus, we consider the failure cascade in a coupled system (smartgrid) comprising a power grid and a cyber network caused by the attack of a cyber malware. The effects of power overloading, contagion, and interdependence between a power grid and a cyber network are taken into consideration in the model. Different coupling patterns and different cyber network structures are compared to study their effcts on the robustness of the coupled system. Simulation results show that cyber coupling can intensify both the extent and rapidity of power blackouts, and that the cyber network structure and the coupling patterns affect the propagation of cascading failures in cyber-coupled power networks.
|Description:||xx, 144 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P EIE 2017 Zhang
|URI:||http://hdl.handle.net/10397/71554||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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Citations as of Mar 18, 2018
Citations as of Mar 18, 2018
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