Date of Graduation
5-2021
Document Type
Thesis
Degree Name
Bachelor of Science in Computer Engineering
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Li, Qinghua
Committee Member/Reader
Patitz, Matthew
Committee Member/Second Reader
Jin, Dong
Abstract
Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated generation of detection threshold for Long-Short-Term-Memory neural network based detection models. Second, we study the performance of various detection models under low-rate false data injection attacks.
Keywords
Automatic Generation Control; AGC; electric power; key control system; power generation; power system
Citation
Dubasi, Y. R. (2021). Data Forgery Detection in Automatic Generation Control: Exploration of Automated Parameter Generation and Low-Rate Attacks. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/88
Included in
Artificial Intelligence and Robotics Commons, Controls and Control Theory Commons, Data Storage Systems Commons, Information Security Commons, Other Computer Sciences Commons, Power and Energy Commons, Signal Processing Commons, Systems and Communications Commons
