abstract: Understanding the graphical structure of the electric power system is important
in assessing reliability, robustness, and the risk of failure of operations of this criti-
cal infrastructure network. Statistical graph models of complex networks yield much
insight into the underlying processes that are supported by the network. Such gen-
erative graph models are also capable of generating synthetic graphs representative
of the real network. This is particularly important since the smaller number of tradi-
tionally available test systems, such as the IEEE systems, have been largely deemed
to be insucient for supporting large-scale simulation studies and commercial-grade
algorithm development. Thus, there is a need for statistical generative models of
electric power network that capture both topological and electrical properties of the
network and are scalable.
Generating synthetic network graphs that capture key topological and electrical
characteristics of real-world electric power systems is important in aiding widespread
and accurate analysis of these systems. Classical statistical models of graphs, such as
small-world networks or Erd}os-Renyi graphs, are unable to generate synthetic graphs
that accurately represent the topology of real electric power networks { networks
characterized by highly dense local connectivity and clustering and sparse long-haul
links.
This thesis presents a parametrized model that captures the above-mentioned
unique topological properties of electric power networks. Specically, a new Cluster-
and-Connect model is introduced to generate synthetic graphs using these parameters.
Using a uniform set of metrics proposed in the literature, the accuracy of the proposed
model is evaluated by comparing the synthetic models generated for specic real
electric network graphs. In addition to topological properties, the electrical properties
are captured via line impedances that have been shown to be modeled reliably by well-studied heavy tailed distributions. The details of the research, results obtained and
conclusions drawn are presented in this document. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015
| Identifer | oai:union.ndltd.org:asu.edu/item:34886 |
| Date | January 2015 |
| Contributors | HU, JIALE (Author), Sankar, Lalitha (Advisor), Vittal, Vijay (Committee member), Scaglione, Anna (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 64 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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