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153945 https://hdl.handle.net/2286/R.I.34886 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2015 xii, 51 pages : illustrations (some color) Masters Thesis Academic theses Text eng Hu, Jiale Sankar, Lalitha Vittal, Vijay Scaglione, Anna Arizona State University Partial requirement for: M.S., Arizona State University, 2015 Includes bibliographical references (pages 50-51) Field of study: Electrical engineering Understanding the graphical structure of the electric power system is important<br/><br/>in assessing reliability, robustness, and the risk of failure of operations of this criti-<br/><br/>cal infrastructure network. Statistical graph models of complex networks yield much<br/><br/>insight into the underlying processes that are supported by the network. Such gen-<br/><br/>erative graph models are also capable of generating synthetic graphs representative<br/><br/>of the real network. This is particularly important since the smaller number of tradi-<br/><br/>tionally available test systems, such as the IEEE systems, have been largely deemed<br/><br/>to be insucient for supporting large-scale simulation studies and commercial-grade<br/><br/>algorithm development. Thus, there is a need for statistical generative models of<br/><br/>electric power network that capture both topological and electrical properties of the<br/><br/>network and are scalable.<br/><br/>Generating synthetic network graphs that capture key topological and electrical<br/><br/>characteristics of real-world electric power systems is important in aiding widespread<br/><br/>and accurate analysis of these systems. Classical statistical models of graphs, such as<br/><br/>small-world networks or Erd}os-Renyi graphs, are unable to generate synthetic graphs<br/><br/>that accurately represent the topology of real electric power networks { networks<br/><br/>characterized by highly dense local connectivity and clustering and sparse long-haul<br/><br/>links.<br/><br/>This thesis presents a parametrized model that captures the above-mentioned<br/><br/>unique topological properties of electric power networks. Specically, a new Cluster-<br/><br/>and-Connect model is introduced to generate synthetic graphs using these parameters.<br/><br/>Using a uniform set of metrics proposed in the literature, the accuracy of the proposed<br/><br/>model is evaluated by comparing the synthetic models generated for specic real<br/><br/>electric network graphs. In addition to topological properties, the electrical properties<br/><br/>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<br/><br/>conclusions drawn are presented in this document. Electrical Engineering electrical distance Power network Synthetic graph generation Electric power systems Electric network topology--Graphic methods. Electric network topology Cluster-and-connect: an algorithmic approach to generating synthetic electric power network graphs