ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Filtering by
- All Subjects: Electric power systems
- Creators: Vittal, Vijay
The test bed feeder model representing a real operational distribution feeder is developed in OpenDSS and the feeder modeling takes into consideration the ob-jective of analysis and frequency of interest. Extensive metering infrastructure and measurements are utilized for validation of the model at harmonic frequencies. The harmonic study performed is divided into two sections: study of impact of non-linear loads on total harmonic voltage and current distortions and study of impact of PV resources on high frequency spectral distortion in voltages and cur-rents. The research work incorporates different harmonic study methodologies such as harmonic and high frequency power flow, and frequency scan study. The general conclusions are presented based on the simulation results and in addition, scope for future work is discussed.
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.