Record Details
Power Grid Modeling Using Graph Theory and Machine Learning Techniques
ScholarsArchive at Oregon State University
| Field | Value |
|---|---|
| Title | Power Grid Modeling Using Graph Theory and Machine Learning Techniques |
| Names |
Duncan, Daniel
(creator) Cotilla-Sanchez, Eduardo (advisor) |
| Date Issued | 2015-06-04 (iso8601) |
| Note | Honors Bachelor of Science (HBS) |
| Abstract | Graphs have a wide variety of real-world applications. In the area of social networks, graphs are composed of individuals and their relationships with others. Analysis of social networks led to the discovery of the small-world phenomena, which is also known as six degrees of separation. Our analysis is focused on discovering the properties of real-world power grids. Analyzing the structure of power grids is useful for protecting it from various forms of attack. Power grid failure is a devastating event that can be triggered by certain events. We describe what randomly generated grids are similar to real power grids, so that tests can be run with the randomly generated experimental model grids. We also evaluate the clustering of similar nodes within a power grid so that computers can have a better understanding of the power grid's operation at any point in time. |
| Genre | Thesis |
| Access Condition | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ |
| Topic | Graphs |
| Identifier | http://hdl.handle.net/1957/56036 |
