Graduate Thesis Or Dissertation
 

Development of Machine Learning Tools for Increased Reliability of the Electrical Grid

Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/j098zg72j

Descriptions

Attribute NameValues
Creator
Abstract
  • The electrical grid serves as one of the major lifelines to our society. We have come to expect electricity to be available when we want it. But the system that delivers power to us is very complex and made up of many individual pieces. Maintaining and operating this system reliably is a challenge. Increases in the amount of digital technology on the grid has been both a benefit, in increased amounts of data in order to monitor the system, and a detriment as it provides an increased number of places to attack the grid. This thesis will detail the development of two reliability methodologies. The first seeks to ensure the security of the grid by ascertaining the validity of the data that is being reported by equipment on the grid. The contribution of this section is the formulation of engineered features for a Support Vector Machine that detects spoofed data on the grid. The second proposed methodology resulted in a grid reliability software tool that leverages machine learning clustering algorithms, namely K-means combined with the electrical distance attributes of the system, to determine Voltage Control Areas (VCAs) in a system. These voltage control areas then can help operators approach the data coming from their monitoring systems in a structured way that better aligns with their service zones to operate them more reliably.
License
Resource Type
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Rights Statement
Publisher
Peer Reviewed
Language

Relationships

Parents:

This work has no parents.

In Collection:

Items

Thumbnail Title Date Uploaded Visibility Actions
file details DieterKeatonJ2018.pdf 2018-06-13 Public Download