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Virginia Tech

Anomaly Detection for Control Centers

Abstract

dc:description.abstract

The control center is a critical location in the power system infrastructure. Decisions regarding the power system’s operation and control are often made from the control center. These control actions are made possible through SCADA communication. This capability however makes the power system vulnerable to cyber attacks. Most of the decisions taken by the control center dwell on the measurement data received from substations. These measurements estimate the state of the power grid. Measurement-based cyber attacks have been well studied to be a major threat to control center operations. Stealthy false data injection attacks are known to evade bad data detection. Due to the limitations with bad data detection at the control center, a lot of approaches have been explored especially in the cyber layer to detect measurement-based attacks. Though helpful, these approaches do not look at the physical layer. This study proposes an anomaly detection system for the control center that operates on the laws of physics. The system also identifies the specific falsified measurement and proposes its estimated measurement value.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Electrical Engineering
Department dc:contributor.department
Electrical and Computer Engineering
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2024

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Gyamfi, Cliff Oduro
Chair dc:contributor.committeechair
  • Liu, Chen-Ching
Committee members dc:contributor.committeemember
  • Centeno, Virgilio A.
  • Mehrizi-Sani, Ali

Subjects

dc:subject × 5

Rights

dc:rights
Statement dc:rights
  • CC0 1.0 Universal
Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/10919/119326
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/119326

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Gyamfi, Cliff Oduro. Anomaly Detection for Control Centers. masters thesis, Virginia Tech, 2024. https://hdl.handle.net/10919/119326